In this paper, we studied if human emotions (anger, disgust, fear, happiness, and sadness) can be recognized using the force applied to a 6-axis force sensor. We asked human subjects to apply force to the force sensor to convey their emotions. We used the obtained data to extract 11 feature parameters such as the contact duration, the max magnitude of resultant force, and the mean magnitude of resultant force. Using these feature parameters, we trained a random forest classifier and obtained a classification accuracy of 55.5%. We also investigated how well human subjects could recognize the five emotions by the force applied to their shoulders. The human subject's overall accuracy was 55.9%, which was similar to the classifier's accuracy.

In this paper, we present a novel approach to 2D street map-based localization for mobile robots that navigate mainly in urban sidewalk environments. Recently, localization based on the map built by Simultaneous Localization and Mapping (SLAM) has been widely used with great success. However, such methods limit robot navigation to environments whose maps are prebuilt. In other words, robots cannot navigate in environments that they have not previously visited. We aim to relax the restriction by employing existing 2D street maps for localization. Finding an exact match between sensor data and a street map is challenging because, unlike maps built by robots, street maps lack detailed information about the environment (such as height and color). Our approach to coping with this difficulty is to maximize statistical dependence between sensor data and the map, and localization is achieved through maximization of a Mutual Information-based criterion. Our method employs a computationally efficient estimator of Squared-loss Mutual Information through which we achieved near real-time performance. The effectiveness of our method is evaluated through localization experiments using real-world data sets.

In this paper, we describe technologies of the robot Accelite that is the winner of the RoboCup2015 Humanoid KidSize League. In 2015, the rules of the humanoid league were changed dramatically. The ball was changed from a monochromatic orange ball to a real soccer ball. Furthermore, the goal color was also changed from yellow to white. Therefore, the soccer robots in the humanoid league were not able to detect the ball based on just colors. We developed some detection methods and verified their effectiveness. Consequently, our robots detected the ball robustly and we succeeded in the technical challenge “Goal-Kick from Moving Ball”. In RoboCup 2015, the field of play was also changed from a punch carpet to an artificial grass. Because the pile length of the artificial grass in RoboCup2015 was long, our robots were not able to walk robustly when we started to use the field. We also explain how we solved the problem. We also discuss how we succeeded in the technical challenges.

In this study, we present a novel road recognition method using a single image for mobile robot navigation. Vision-based road recognition in outdoor environments remains a significant challenge. Our approach exploits digital street maps, the robot position, and prior knowledge of the environment. We segment an input image into superpixels, which are grouped into various object classes such as roadway, sidewalk, curb, and wall. We formulate the classification problem as an energy minimization problem and employ graph cuts to estimate the optimal object classes in the image. Although prior information assists recognition, erroneous information can lead to false recognition. Therefore, we incorporate localization into our recognition method to correct errors in robot position. The effectiveness of our method was verified through experiments using real-world urban datasets.

In this study, we aim to achieve autonomous navigation for robots in environments that they have not previously visited. Many of the existing methods for autonomous navigation require a map to be built beforehand, typically by manually navigating the robot. Navigation without maps, i.e., without any prior information about the environment, is very difficult. We propose to use existing digital street maps for autonomous navigation. Nowadays digital street maps (e.g., those provided by Google Maps) are widely available and used routinely. Reuse of existing maps for robots eliminates extra cost of building maps. One of the difficulties in using existing street maps is data association between a robot's observation and the map, because the physical entities that correspond to the boundary lines in the map are unknown. We address this issue by using region annotations such as roads and buildings and prior knowledge. We introduce a probabilistic framework that simultaneously estimates a robot's position and the road's boundaries.We evaluated our method in complex urban environments. Our method successfully localized in environments that includes both roadways and pedestrian walkways.

We propose a novel localization method for outdoor mobile robots using High Dynamic Range (HDR) vision technology. To obtain an HDR image, multiple images at different exposures is typically captured and combined. However, since mobile robots can be moving during a capture sequence, images cannot be fused easily. Instead, we generate a set of keypoints that incorporates those detected in each image. The position of the robot is estimated using the keypoint sets to match measured positions with a map. We conducted experimental comparisons of HDR and auto-exposure images, and our HDR method showed higher robustness and localization accuracy.

This paper presents a 3D laser scanner that can gaze at an arbitrary region. By modulating the secondary rotation speed of roundly swinging 3D laser scanner, it gazes at a specific region and measures with high density measurement points. The proposed method uses a secondary rotation motor to control the measurement point density and no extra motor is required. The 3D scanner can keep the field of view of and scan cycle time while gazing at any region. The system is evaluated on experimental targets for different scenarios using a mobile robot.

This paper proposes a new localization method for outdoor navigation using a stereo camera only. Vision-based navigation in outdoor environments is still challenging because of large illumination changes. To cope with various illumination conditions, we use 2D occupancy grid maps generated from 3D point clouds obtained by a stereo camera. Furthermore, we incorporate salient line segments extracted from the ground into the grid maps. This grid map building is not much affected by illumination conditions. On the grid maps, the robot poses are estimated using a particle filter that combines visual odometry and map-matching. Experimental results showed the effectiveness and robustness of the proposed method under various weather and illumination conditions.

This paper proposes a light-weight sensor platform that consists of gyro-assisted odometry and a 3D laser scanner for localization of human-scale robots. The gyro-assisted odometry provides highly accurate positioning only by dead-reckoning. The 3D laser scanner has a wide field of view and uniform measuring-point distribution. Robust and computationally inexpensive localization is implemented on the sensor platform using a particle filter on a 2D grid map generated by projecting 3D points on to the ground. The system uses small and low-cost sensors, and can be applied to a variety of mobile robots in human-scale environments. Outdoor navigation experiments were performed at the Tsukuba Challenge 2009, which is an open proving ground for human-scale robots. Our robot successfully navigated the assigned 1-km course in a fully autonomous mode multiple times.