Mobile robots can be used in numerous outdoor tasks such as patrolling, delivery and military applications. In order to deploy mobile robots in this kind of environment, where there are different challenges like slopes, elevations, or even holes, they should be able to detect such challenges and determine the best path to accomplish their tasks. In this paper, we are proposing an exploration approach based on potential fields with local distortions, in which we define preferences in uneven terrains to avoid high declivity regions without compromising the best path. The approach was implemented and tested in simulated environments, considering a ground robot embedded with two 2D LIDAR sensors, and the experiments demonstrated the efficiency of our method.

Service robots are becoming a reality across homes. Still, the range of applications supported by such robots remains tied to their ability to self-localize in the environment. Man-made constructions often have a documented blueprint which can be used as input information for robot localization and smart home applications. However, home environments commonly include movable objects and furniture, which can make localization a complicated task, especially for forward-facing horizontal range-finders. In this paper, we present a new and effective global localization approach for home environments which adapts the notion of free space density to a camera pointing to the ceiling. We exploit the available blueprint information, as well as evidence that ceiling vision can provide robust localization information, even in the presence of occlusions. We perform real-world experiments using a robotic vacuum cleaner equipped with an upward-facing camera in two different apartments across multiple trajectories and compare the proposed method with competing approaches. Our solution shows superior localization results using maps where neither furniture or movable objects are not modeled.

Proper place recognition on an environment that can change over time is fundamental for long-term SLAM. In such scenarios the observations obtained in the same region can drastically differ due to changes caused by semi-static objects, such as doors, furniture, etc. In this work, we extend a strategy that represents environment regions using words, based on spatial density information extracted from laser readings. This time, in order to deal with changes in the environment, our method not only builds words representing the real observations made by the robot, but also alternative multi-level words to account for possible changes in a place's observations generated by non-static objects. Place recognition is made by searching matches of sequences of N consecutive words (both real or alternatives). Experiments performed in real and simulated scenarios are shown, and demonstrate the advantages associated to the use of multi-level words.

An autonomous robot requires a map of the environment for many tasks. Yet, in many cases, this map is unavailable and the robot must build one in real-time, in the so-called integrated exploration task. Several integrated exploration approaches adopt some sort of loop-closing strategy combined with an online simultaneous localization and mapping (SLAM) technique. This is important because the robot can reduce the uncertainty about its pose by revisiting known areas. One solution for environment exploration is to use the vector field computed from the numeric solution of a Boundary Value Problem (BVP). This approach is called BVP Path Planner and generates smooth and free of local minima potential fields. However, this planner cannot actively close loops and does not scale well in large scenarios. In this paper we present a technique which performs active loop closure using the BVP Path Planner. Our proposal takes advantage of the potential of unexplored regions, and induces the robot to close loops by placing dynamic barriers at the visited space. The update of the potential field is boosted using a local window charged by a Voronoi diagram of the environment containing global information. We show through experimental results the effectiveness of the technique with a thorough discussion of its characteristics.

Estimating the robot localization is a fundamental requirement for applications in robotics. For many years, Monte Carlo Localization (MCL) has been one of the most popular approaches to solve the global localization when using range finders, like sonars or lasers. It generally weights the estimates about the robot state by comparing raw sensor readings with simulated readings computed for each estimate. In this paper, we propose an observation model for localization that associates a kernel density estimate (KDE) to each point in the space. This single-valued density measure is independent of orientation, what allows an efficient pre-caching step, substantially boosting the computation time of the process. Using the gradient of the densities field, our strategy is able to estimate orientation information that helps to restrict the localization search space. Additionally, we can combine densities obtained by kernels of different sizes and profiles to improve the quality of the acquired information. We show through experiments in comparison with traditional approaches that our method is efficient, even working with large sets of particles, and effective.

Place recognition is the frond-end of Simultaneous Localization and Mapping (SLAM). Topological representations depend on good association of vertices, which ultimately depends on the front-end. In this paper, we consider a robot lost in an unknown environment trying to construct a topological map to localize itself using a laser range finder and odometry information. The algorithm makes use of an efficient observation model based on kernel density estimates (KDEs) to detect loops. The observation model separates the map into regions denominated words, classified based on the density of free space, number of observations and segment orientation. Loop closing results from the matching of sequences of N consecutive words (n-grams). The proposed approach is orders of magnitude faster than a sequence of Iterative Closest Point (ICP) matches. The method is evaluated varying input parameters in real and simulated scenarios.

Integrated exploration is the most complete task in mobile robotics, and corresponds to the union of mapping, localization and motion planning. A powerful integrated exploration solution must take into account decisions that improve the quality of the map construction, such as closing loops, at the same time that the environment is explored. Potential fields and boundary value problems (BVP) have been used with success in tasks of planning, localization and exploration, but not yet in integrated strategies. In this paper, we present an integrated exploration strategy using a time varying BVP-based exploration. Our strategy consists of creating potential rails that guide the robot to regions that are either unexplored or were visited a long time ago. We also apply local distortions in the potential field to generate a loop closure strategy. Experimental results demonstrate that our method improves the quality of the map construction, keeping the balance between revisiting and exploratory activities.

Unambiguous definition of spatial position and orientation has crucial importance for robotics. In this paper we propose an ontology about positioning. It is part of a more extensive core ontology being developed by the IEEE RAS Working Group on ontologies for robotics and automation. The core ontology should provide a common ground for further ontology development in the field. We give a brief overview of concepts in the core ontology and then describe an integrated approach for representing quantitative and qualitative position information.

Simultaneous Localization and Mapping (SLAM) is one of the most difficult tasks in mobile robotics. While the construction of consistent and coherent local solutions is simple, the SLAM remains a critical problem as the distance travelled by the robot increases. To circumvent this limitation, many strategies divide the environment in small regions, and formulate the SLAM problem as a combination of multiple precise submaps. In this paper, we propose a new submap-based particle filter algorithm called Segmented DP-SLAM, that combines an optimized data structure to store the maps of the particles with a probabilistic map of segments, representing hypothesis of submaps topologies. We evaluate our method through experimental results obtained in simulated and real environments.

Most human computer interfaces impose a certain level of additional cognitive load to the user task. In this project we study the use of VR to evaluate the user competency for safe behavior in work environments. We propose an assessment of two interfaces with different levels of cognitive load for locomotion in a risk perception task. We describe user experiments with two similar groups of subjects, each using one of the interfaces. We identified important open questions on the design of this kind of interface.

In this demo we present an immersive virtual reality (VR) simulator to measure risk perception in realistic working environments. It provides the ideal environment to expose people to danger with no chances to cause them any harm. However, whereas in the physical work environment workers have to perform their jobs (primary task) and be safe (secondary task), in a VR simulator they still have to deal with an additional source of cognitive load: the user interface.

Collaboration in 3D environments has the main goal of attaining a high degree of teamwork by exposing the team to a suitable level of immersion. However, users within virtual environments have limitations to perceive the same stimuli of the real world. Particularly, the feeling of presence and nearness of the other is difficult to be generated in VR systems. This poster presents a work in progress that shows our current results on evaluating the impact of three different proximity cues for generating awareness of the presence of other. The design of each cue aims at independently stimulating the senses of sight, hearing and touch. The experiment design includes the comparison with a baseline condition in virtual environments where no specific stimulation is applied.

Any human-computer interface imposes a certain level of cognitive load to the user task. Analogously, the task itself also imposes different levels of cognitive load. It is common sense in 3D user interfaces research that a higher number of degrees of freedom increases the interface cognitive load. If the cognitive load is significant, it might compromise the user performance and undermine the evaluation of user skills in a virtual environment. In this paper, we propose an assessment of two immersive VR interfaces with varying degrees of freedom in two VR tasks: risk perception and basic object selection. We examine the effectiveness of both interfaces in these two different tasks. Results show that the number of degrees of freedom does not significantly affect a basic selection task, but it affects risk perception task in an unexpected way.