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.