Interactions between pedestrians and autonomous personal mobility vehicle (APMV) will increase with the popularity of autonomous driving systems. However, when the APMVs are applied in a mixed traffic environment after manual driving PMV (MPMV) have been popular, pedestrians may feel unsafe in the interactions when they are uncertain about the driving intention of the APMV. This study seeks to find a surrogate measure for pedestrians’ understanding of driving intention and perceived safety during the interaction with an APMV. We conducted an experiment to measure the gaze duration and subjective evaluations of the participants when they interacted with a PMV in manual and autonomous driving modes. Pedestrians fixed their gaze at the APMV longer when they did not accurately understand the driving intention than when they understood it. Furthermore, the pedestrians perceived danger when they did not clearly understand the driving intention of the APMV. Besides, these factors were different when pedestrians interact with an MPMV and an APMV.

This paper proposes a method to compute camera 6 DoF poses to achieve a user defined coverage. The camera placement problem is modeled as a combinatorial optimization where given the maximum number of cameras, a camera set is selected from a larger pool of possible camera poses. We propose to minimize the squared error between the desired and the achieved coverage, and formulate the non-linear cost function as a mixed integer linear programming problem. A camera lens model is utilized to project the camera's view on a 3D voxel map to compute a coverage score which makes the optimization problem in real environments tractable. Experimental results in two real retail store environments demonstrate the better performance of the proposed formulation in terms of coverage and overlap for triangulation compared to existing methods.

We present a method to predict humans in unexplored map areas given limited observations of the environment. We used a geometric representation of the environment based on cost maps and semantic room categorization. Human density distributions were generated using a human tracker based on LiDAR data recorded by a mobile robot. A Gaussian Process (GP) regression model was created to predict human density in surrounding unobserved map locations. GP prediction performance was evaluated on density data recorded in a series of ten simulations of 25 persons in an office setting, and in real-world robot deployments in an office-like environment. Experimental results demonstrate that the current method can predict human locations with an accuracy average of 70%.

This paper presents a comparison of driving behavior modeling methods based on hidden Markov models (HMMs) with driver's eye-gaze measurement and ego-vehicle localization. Original HMMs are sometimes insufficient to model real-world scenarios. To overcome these limitations, extended HMMs have been proposed, e.g., autoregressive input-output HMMs (AIOHMMs). This paper first details AIOHMMs and presents ways to use them for driving behavior modeling. We compare the performance for behavior modeling and maneuver discrimination for six types of HMMs. The driving data for this work was gathered in our university campus with a car-like vehicle. Experimental results suggest that the hidden states can properly represent the average of the driving actions when the driving behaviors are accurately modeled by the HMMs. It is also suggested that surrounding and past information can be used to flexibly model the relationship between driving actions and related information.

Localization robustness against environment dynamics is significant for robots to achieve autonomous navigation in unmodified environments. A basic method of improving the robustness of a robot is considering the sensor observations obtained from mapped obstacles and using them for localizing the robot's pose. This study proposes an observation model that considers the class of sensor observations, where “class” categorizes the sensor observations as those obtained from mapped and unmapped obstacles. In the proposed approach, the robot's pose and the class are estimated simultaneously. As a result, the robot's pose can be localized using the sensor observations obtained only from mapped obstacles. First, we evaluated the performance of the proposed approach using simulations. Further, we tested the proposed approach in a real-world mobile robot navigation competition, called “Tsukuba Challenge,” held in Japan. The robustness and effectiveness of the proposed approach against environment dynamics were verified from the experimental results.

In this paper we present a data driven approach for safe and smooth autonomous navigation of a personal mobility vehicle (PMV) when facing moving obstacles such as people and bicycles in public pedestrian paths. In a period of three months, data from five different persons driving the robotic PMV in an outdoor environment while facing pedestrians were collected. 2465 clean tracks around the vehicle together with PMVs trajectories were collected. We performed an analysis of the parameters involved for human-driven smooth navigation. Relevant parameters regarding PMV-Human interaction included distance to moving objects, passing side and velocities. Moreover, data suggests the existence of a social navigational distance for the PWv. For autonomous navigation we implemented a Frenet planner to achieve safe and smooth navigation for the passenger and pedestrians around. Experimental results in real pedestrian paths show that the PMV is capable of smoothly following its path while facing pedestrians and bicycles.

This paper proposes a method for estimation of the reliability of vehicle localization results. We previously proposed a fault detection method for indoor mobile robots using a convolutional neural network (CNN). Because image data is generally fed to a CNN, we feed image data obtained from the robot pose, occupancy grid map, and laser scan data to the CNN, which decides of whether localization has failed. The previous method also employed a Rao-Blackwellized particle filter to estimate the robot pose and reliability of this estimation simultaneously. However, it was difficult for vehicle robots to use the previous method as creating and processing image data is not a light computation process. In this study, we extend the previous method by improving the data fed to the CNN, thus making it possible for vehicle robots to perform simultaneous localization and estimation. This paper describes in detail the simultaneous estimation and shows that the reliability can be used as an exact criterion for detecting localization failures. Keywords-Vehicle Localization, Reliability

We introduce a framework for detection and classification of spatio-temporal person-object interactions. Our method clusters similar semantic contexts from interactions detected from RGB-D data. 2-D object detection (YOLO) is run on RGB data from a Kinect v2 sensor on a mobile robot navigating an office and observing persons and desk spaces. Person and object detections are converted into 3-D point cloud time series via RGB-Depth co-registration and successive Euclidean and k-means spatial clustering. 3-D person and object point cloud streams are used to create time-series occupancy maps and person-object co-localization maps. From these maps, spatiotemporal correlations between persons and distinct objects are computed. Correlation patterns are clustered using k-means to obtain distinct human-object interactions, i.e. segment semantic context over time. We evaluated the performance of our approach to detect person-object correlations and cluster semantic context by recording 90 30-second RGB-D data episodes, with three persons handling representative objects (books, cups, bottles). Experimental results show that our framework is able to consistently assign semantic context to the same cluster in > 79% of cases (scene frames). Semantic contexts in visual scenes can be distinguished without the need to provide prior information, allowing mobile agents to learn and explore in new environments.

In this paper we present a method to learn how to drive in different types of blind intersections using expert driving data. We cluster different intersections based on the velocity of how drivers approach them, and train a linear SVM classifier for each class of intersection. Through clustering we found that there were three different classes of intersections in typical residential areas in Japan. We used inverse reinforcement learning (IRL) to build a driving model for each type of intersection. The models were trained from 308 trajectories traversed by 5 different drivers. The models and policies were implemented and evaluated in a ROS simulator where the agent is provided a global path, and upon it reaching an intersection, it selects the appropriate trained policy. By doing this, the simulated autonomous vehicle can perform proactive safe driving behaviors when approaching blind intersections.

This paper presents a model of navigational habituation for human passengers riding an autonomous robotic wheelchair. We present an example on how robot technology is utilized to understand human behavior which is useful to build human-like navigation models. The work consists of three parts, data collection from manual driving participants, habituation modeling and autonomous navigation experimentation. Manual driving data collection consisted of recording human driving control behavior using a robotic wheelchair in a complex labyrinth-like environment, while measuring physiological activity and emotional state via subjective reports (questionnaires). The purpose of this first part was to collect a sufficient representation of the evolution of human driving behavior in a complex environment (23 participants), such that a numerical model could be found to approximate the trend in velocity usage; velocity usage characterizes navigational habituation. The complex environment habituation model was tested and evaluated, on a different set of 16 participants, in a fully autonomous navigation setting where the robotic wheelchair followed a pre-recorded path. Self-reports and physiological measures confirmed that the proposed habitation velocity regulation model provides a more comfortable ride compared to slow and fast pre-defined static velocity profiles.

This paper presents a model for safe driving at blind intersections and its integration to a local planner based on a Frenet frame. The model predicts potential moving obstacles from blind intersections to proactively slow down to avoid potential collisions. The derivation of the model is described and its parameters are detailed. The local planner computes smooth trajectories with smooth velocity profiles so that the vehicle can follow the paths without jerk and sudden accelerations resulting in safe and comfortable navigation. Experimental results in simulation and in the real field with an autonomous car, show that the proposed predictive driving framework can reproduce human expert driver's trajectories and velocities when facing blind intersections.

In this paper, we present a localization approach that is based on a point-cloud matching method (normal distribution transform “NDT”) and road-marker matching based on the light detection and ranging intensity. Point-cloud map-based localization methods enable autonomous vehicles to accurately estimate their own positions. However, accurate localization and “matching error” estimations cannot be performed when the appearance of the environment changes, and this is common in rural environments. To cope with these inaccuracies, in this work, we propose to estimate the error of NDT scan matching beforehand (off-line). Then, as the vehicle navigates in the environment, the appropriate uncertainty is assigned to the scan matching. 3D NDT scan matching utilizes the uncertainty information that is estimated off-line, and is combined with a road-marker matching approach using a particle-filtering algorithm. As a result, accurate localization can be performed in areas in which 3D NDT failed. In addition, the uncertainty of the localization is reduced. Experimental results show the performance of the proposed method.

This paper presents a model for velocity control in blind corners and intersections based on expert driver data. Accurate expert driver data was collected with a car equipped with a 3D LiDAR and high definition maps. A model based on human expert driver data is used to control the velocity of the ego-vehicle when facing blind intersections. The model regulates ego-vehicle velocity based on the visibility of the road at the blind intersection. As the vehicle approximates the intersection and crossing roads are not visible, the vehicle slows down, then as the roads become visible the vehicle accelerates. Experimental results show the performance of the velocity model compared towards 270 trajectories taken from 7 expert drivers towards 6 different intersections without mandatory stops.

This paper presents a two-step framework for creating the three-dimensional (3-D) sound map of an environment with a mobile robot. The first step is the creation of a map that describes the geometry of the environment. The second step is the addition of the acoustic information to the geometric map. The result is a sound map that shows the probability of emitting sound for all the structures in the environment. To build the sound map, a mobile robot equipped with a microphone array drives through the mapped environment. During this drive, the acoustic information gathered by the microphone array is accumulated in a probabilistic manner. First, the likelihood of sound source presence in a set of directions is evaluated from the acoustic power received from these directions. Then, using an estimate of the robot's pose, an acoustic ray casting procedure transfers this likelihood to the structures in the geometric map. Finally, the probability that these structures emit sound is updated accordingly to the likelihood. Experimental results show that the sound maps are: accurate as it was possible to localize sound sources in 3-D and practical as different types of environments were mapped.

This work proposes a Human-Comfortable Path Planner (HCoPP) system for autonomous passenger vehicles. The aim is to create a path planner that improves the feeling of comfort of the passenger, this topic is different from collision free planning and it has not received much attention. For this purpose, in addition to the shortest distance constraint conventionally used in path planning, constraints related to relevant environmental features are introduced. For straight segments, the constraint is based on the lane-circulation pattern preferred by humans. In curved segments and intersections, the constraint takes into account the visibility. A multi-layered cost map is proposed to integrate these additional constraints. To compute the human-comfortable path, a graph search algorithm was implemented. The evaluation of the proposed approach was conducted by having 30 participants riding an autonomous robotic wheelchair. The paths computed by the proposed path planner were compared towards a state of the art shortest-distance path planner implemented in the navigation stack of ROS. Experimental results show that the paths computed by the proposed approach are perceived as more comfortable.

This paper presents a method for representing acoustic information with point clouds by tying it to geometrical features. The motivation is to create a representation of this information that is well suited for mobile robotic applications. In particular, the proposed approach is designed to take advantage of the use of multiple coordinate frames. As an illustrative example, we present a way to create an audio augmented point cloud by adding estimated audio power to the point cloud created by a RGB-D camera. A few applications of this method are presented.

This paper presents a work on mapping the use of space by humans in long periods of time. Daily geometric maps with the same coordinate frame were generated with SLAM, and in a similar manner, daily affordance density maps (places people use) were generated with the output of a human tracker running on the robot. The contribution of the paper is two-fold: an approach to detect geometric changes to cluster them in similar geometric configurations and the building of geometric and affordance composite maps on each cluster. This approach avoids the loss of long term retrieved information. Geometric similarity was computed using a normal distance approach on the maps. The analysis was performed on data collected by a mobile robot for a period of 4 months accumulating data equivalent to 70 days. Experimental results show that the system is capable of detecting geometric changes in the environment and clustering similar geometric configurations.

This work presents a human-robot cooperative approach for infrastructure inspection. The goal is to create a robot that assists the human inspector during hammer sounding inspections. Hammer sounding is a frequently used inspection technique that detects invisible defects under the surface of concrete by striking the surface with a hammer and listening the resulting sound. The conventional hammer sounding inspection is time-consuming, and there is no convenient way to represent exhaustively the test results. The proposed approach solves these two problems by having an assistant robot following the inspector, and always being able to look at the hammer impact position. The assistant robot accurately estimates the position of the impact in real-time and creates a detailed representation of the test results. Experimental results show the process for creating the detailed inspection report. The accuracy of the human-robot cooperative approach is evaluated for a real world application. The average error of the impact point estimation was 32 millimeters and the standard deviation was 30 millimeters.

This paper presents a study on intention communication in a navigational context using a robotic wheelchair. The robotic wheelchair uses light projection to communicate its motion intentions. The novelty of the work is threefold: the communication of robot intentions to the passenger, the consideration of passenger and robot as a group (“in-group”) [1] who share motion intentions and the communication of the in-group intentions to other pedestrians (the “out-group”). A comparison in an autonomous navigation task where the robotic wheelchair autonomously navigates the environment with and without intention communication was performed showing that passengers and walking people found intention communication intuitive and helpful for passing by actions. Evaluation results significantly show human participant preference for having navigational intention communication for the wheelchair passenger and the person passing by it. Quantitative results show the motion of the person passing by the wheelchair with intention communication was significantly smoother compared to without intention communication.

Walking side by side is a common situation when we go from one place to another with another person while talking. Our previous study reported a basic mechanism for side-by-side walking, but in the previous model it was crucial that each agent knew where he or she was going, i.e. the route to the destination. However, we have recognized the need to model the situation where one of the agents does not know the destination. The extended model presented in this work has two states: leader-follower state and collaborative state. Depending on whether the follower agent has obtained a reliable estimate of the route to follow, the walking agents transition between the two states. The model is calibrated with trajectories acquired from pairs of people walking side by side, and then it is tested in a human-robot interaction scenario. The results demonstrate that the new extended model achieves better side-by-side performance than a standard method without knowledge of the subgoal.

This paper models the concept of the "territory" of shops. First, we interviewed three shopkeepers and found that they perceived the space near their shop as their territory and that they interpreted some types of behaviors as invasive. Second, we confirmed that potential visitors share this notion of territory. We also confirmed that the size of the territory depends on the characteristics of a shop's facade. While there is little territory in front of walls, there is more territory in front of shelves and entrances. Our robot traversed around two real shopping malls that included 50 shops and took 3-D scans of their environment shapes. Each shop's facade was analyzed and the shop territory was computed. The computation results match people's perception. The recognition rate accuracy reached 93.5% for the territory areas. User evaluations in a virtual shop environment confirmed that a robot with a territory model behaves better than one without it.

This paper presents a framework for creating a 3D map of an environment that contains the probability of a geometric feature to emit a sound. The goal is to provide an automated tool for condition monitoring of plants. The map is created by a mobile platform equipped with a microphone array and laser range sensors. The microphone array is used to estimate the sound power received from different directions whereas the laser range sensors are used for estimating the platform pose in the environment. During navigation, a ray casting method projects the audio measurements made onboard the mobile platform to the map of the environment. Experimental results show that the created map is an efficient tool for sound source localization.

This work introduces a 3D visibility model for comfortable autonomous vehicles. The model computes a visibility index based on the pose of the wheelchair within the environment. We correlate this index with human navigational comfort (discomfort) and we discuss the importance of modeling visibility to improve human riding comfort. The proposed approach models the 3D visual field of view combined with a two-layered environmental representation. The field of view is modeled with information from the pose of the robot, a 3D laser sensor and a two-layered environmental representation composed of a 3D geometric map with traversale area information. Human navigational discomfort was extracted from participants riding the autonomous wheelchair. Results show that there is fair correlation between poor visibility locations (e.g., blind corners) and human discomfort. The approach can model places with identical traversable characteristics but different visibility and it differentiates visibility characteristics according to traveling direction.

This paper presents a framework for making a mobile robot aware of an entity in the blind region of its laser range finders when that entity emits sound. First in a mapping stage, a 3D description of the environment that contains information about acoustic reflection is created. Then during operation, the robot combines estimated directions of arrival of sound with this 3D description to detect entities that are not visible by line of sight sensors but could be heard because of sound reflections. Using this approach, it is possible to restrict the hypothesis about the position of a sound emitting entity in the blind region to a small set of candidate depth values.

This work proposes a model for human habituation while riding a robotic wheelchair. We present and describe the concept of human navigational habituation which we define as the human habituation to repetitively riding a robotic wheelchair. The approach models habituation in terms of preferred linear velocity based on the experience of riding a wheelchair. We argue that preferred velocity changes as the human gets used to riding on the wheelchair. Inexperienced users initially prefer to ride at a slow moderate pace, however the longer they ride they prefer to speed up to a certain comfort level and find initial slower velocities to be tediously “too slow” for their experience level. The proposed habituation model provides passenger preferred velocity based on experience. Human biological measurements, galvanic skin conductance, and participant feedback demonstrate the preference for habituation velocity control over fixed velocity control. To our knowledge habituation modeling is new in the field of autonomous navigation and robotics.

This paper presents a computational model for side-by-side walking within human-robot interaction (HRI). In this work we address the importance of future motion utility (motion anticipation) of two walking partners. Previous studies only considered a robot moving alongside a person without collisions and with simple velocity-based predictions. In contrast, our proposed model includes two major considerations. First, it considers the current goal, modeling side-by-side walking as a process of moving toward a goal while maintaining a relative position with the partner. Second, it takes the partner's utility into consideration; it models side-by-side walking as a phenomenon where two agents maximize mutual utilities rather than only considering a single agent utility. The model is constructed based in a set of trajectories from pairs of people recorded in side-by-side walking; then, parameters of the model were calibrated for a mobile robot and tested in an autonomous robot walking side-by-side with participants. Finally, two evaluations were performed. The first evaluation shows that the proposed model considering mutual utilities performs better than a single utility method and a method that keeps distance from the walking partner. In the second evaluation the proposed method was used for a robot deployed in a shopping mall environment where it demonstrated to be effective.

This paper presents a multi-modal sensor approach for mapping sound sources using an omni-directional microphone array on an autonomous mobile robot. A fusion of audio data (from the microphone array), odometry information and the laser range scan data (from the robot) was used to precisely localize and map the audio sources in an environment. An audio map is created while the robot is autonomously navigating through the environment by continuously generating audio scans with a steered response power (SRP) algorithm. Using the poses of the robot, rays are cast in the map in all directions given by the SRP. Then each occupied cell in the geometric map hit by a ray is assigned a likelihood of containing a sound source. This likelihood is derived from the SRP at that particular instant. Since the localization of the robot is probabilistic, the uncertainty in the pose of the robot in the geometric map is propagated to the occupied cells hit during the ray casting. This process is repeated while the robot is in motion and the map is updated after every audio scan. The generated sound maps were reused and the changes in the audio environment were updated by the robot as it identifies these changes.

This paper presents a method for mapping the radiated sound intensity of an environment using an autonomous mobile platform. The sound intensities radiated by the objects are estimated by combining the sound intensity at the platform's position (estimated with a steered response power algorithm) and the distances to the objects (estimated using laser range finders). By combining the estimated sound intensity at the platform's position with the platform's pose obtained from a particle filter based localization algorithm, the sound intensity radiated from the objects is registered in the cells of a grid map covering the environment. This procedure creates a map of the radiated sound intensity that contains information about the sound directivity. To illustrate the effectiveness of the proposed method, a map of radiated sound intensity is created for a test environment. Then the position and the directivity of the sound sources in the test environment are estimated from this map.

This paper presents a method for detecting moving entities that are in the robot's path but not in the field of view of sensors like laser scanners, cameras or ultrasonic sensors. The proposed system makes use of passive acoustic localization methods which receive information from occluded regions (at intersections or corners) because of the multipath nature of sound propagation. Contrary to the conventional sensors, this method does not require line of sight. In particular, specular reflections in the environment make it possible to detect moving entities that emit sound such as a walking person or a rolling cart. This idea was exploited for safe navigation of a mobile platform at intersections. The passive acoustic localization output is combined with a 3D geometric map of the environment that is precise enough to estimate sound propagation and reflection using ray casting methods. This gives the robot the ability to detect a moving entity out of the field of view of the sensors that require line of sight. Then the robot is able to recalculate its path and waits until the detected entity is out of its path so that it is safe to move to its destination. To illustrate the performance of the proposed method, a comparison of the robot's navigation with and without the audio sensing is provided for several intersection scenarios.

The noninvasive brain-machine interface (BMI) is anticipated to be an effective tool of communication not only in laboratory settings but also in our daily livings. The direct communication channel created by BMI can assist aging societies and the handicapped and improve human welfare. In this paper we propose and experiment a BMI framework that combines BMI with a robotic house and autonomous robotic wheelchair. Autonomous navigation is achieved by placing waypoints within the house and, from the user side, the user performs BMI to give commands to the house and wheelchair. The waypoint framework can offer essential services to the user with an effectively improved information-transfer rate and is an excellent examples of the fusion of data measured by sensors in the house, which can offer insight into further studies.

Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.

This paper presents a computational model for side-by-side walking for human-robot interaction (HRI). In this work we address the importance of future motion utility (motion anticipation) of the two walking partners.

This paper presents an approach for vehicle localization in the mountainous non-paved roads of a quarry. First, the elaboration of a 3D elevation map using data from dead reckoning, RTK-GPS and a laser sensor is described, where an extended Kalman filter was used for state and covariance estimation. Terrain traversability analysis is performed and scan points are voted in an elevation map which is probabilistically built and updated. Secondly, the vehicle's pose is estimated using a particle filter correcting the position towards the elevation map where GPS information is not used. Finally, experiments were performed in the mountainous gravelled paths of a quarry. Estimated localization results compared towards centimeter precision GPS show that the system offers sub-meter precision.

Environment attributes are perceived or remembered differently according to the perspective used. In this study, two different perspectives, a survey perspective and a route perspective, are explained and discussed. This paper proposes an approach for modeling human environments from a route perspective, which is the perspective used when a human navigates through the environment. The process for route perspective semi-autonomous data extraction and modeling by a mobile robot equipped with a laser sensor and a camera is detailed. Finally, as an example of a route perspective application, a route direction robot was developed and tested in a real mall environment. Experimental results show the advantages of the proposed route perspective model compared with a survey perspective approach. Moreover, the route model is comparable to the performance of an expert person giving route guidance in the mall.

This paper presents an approach for vehicle 3D localization in outdoor woodland environments using a loosely coupled multisensor system. The vehicle 3D dead reckoning is computed using a wheel encoder and an IMU. Dead reckoning is corrected from three different sources: a) using a tilted lidar for road detection and computation of the vehicle position within the road which is then corrected towards a 2D road centerline map given in advance. b) DGPS 2D or 3D data as available. c) Under tree foliage DGPS blackouts commonly occur, specially when measuring height, therefore the use of a barometer for correcting height is proposed. An extended Kalman filter is used for sensor fusion and pose estimation. Finally, the estimated vehicle height is added to the 2D map obtaining a 3D road centerline map with width (measured by the tilted lidar). Thoroughly experimentation on real mountainous woodland paths show the usefulness and robustness of the proposed approach.

This paper describes a 2D localization method for a differential drive mobile vehicle on real forested paths. The mobile vehicle is equipped with two rotary encoders, Crossbow’s NAV420CA Inertial Measurement Unit (IMU) and a NAVCOM SF-2050M GPS receiver (used in StarFire-DGPS dual mode). Loosely-coupled multisensor fusion and sensor fault detection issues are discussed as well. An extended Kalman Filter (EKF) is used for sensor fusion estimation where a GPS noise pre-filter is used to avoid introducing biased GPS data (affected by multi-path). Normalized Innovation Squared (NIS) tests are performed when a GPS measurement is incorporated to reject GPS data outliers and keep the consistency of the filter. Finally, experimental results show the performance of the localization system compared to a previously measured ground truth.

This paper presents and describes the approach for achieving long distance autonomous navigation with a mobile robot on outdoor cluttered pedestrian paths. The task was to finish an event launched by the City of Tsukuba in Japan, called “RealWorld Robot Challenge”, of navigating 1km autonomously in a real environment with real pedestrians and bicycles. The hardware, software and strategy for navigating in cluttered environments is explained. Moreover, the complementary functionality of the overall system where map-based and sensor-based navigation seamlessly change, is presented. The robustness of the system is validated with experimental results.

This paper explains the design and implementation of a discreet landmark capable of modifying its properties in order to be able to differentiate itself from its surrounding environment. In this case, differently from common devices, our landmark does not make use of neither light, wave reflection, or radio frequencies, but it makes use of temperature properties of a peltier device which is able to increase or decrease its temperature. By having different characteristics from its surroundings, in this case a temperature landmark can be robustly found and tracked in complex indoor environments using thermo infrared cameras. The proposed system composition and configuration, landmark finding, extraction and tracking methods are also explained and effectiveness of this system is described and evaluated.

In this paper we present a systematic study of the performance of seven different configurations of GPS on a moving vehicle using three different GPS receivers. The seven different configurations are 1) single frequency code differential DGPS, 2) double frequency code differential DGPS, 3) RTK-GPS receiving RTCM correction from a mobile phone, 4) RTK-GPS receiving RTCM correction information via wireless module from base antenna, 5) StarFire WADGPS, 6) StarFire-DGPS dual mode and 7) StarFire-RTK GPS dual mode. As GPS is mostly used in loosely coupled configurations where receiver output is fused with other sensors, the contribution of this paper is the statistical comparison in two dimensions of different GPS configurations using as performance index availability, precision (using standard deviation parameters of each measurement) and reliability. Two types of study environments were tested, open sky and under tree shading. Time for re-acquiring fix solution for RTK-GPS configurations and re-acquiring dual frequency solution by dual frequency configurations are discussed as well. Finally, experimental measurement results show how under tree shading, biased position data with small covariance can be rejected thresholding measurements with small HDOP values and large number of satellites used for solution.