Visible to the public A Clustering-Based Obstacle Segmentation Approach for Urban Environments

TitleA Clustering-Based Obstacle Segmentation Approach for Urban Environments
Publication TypeConference Paper
Year of Publication2015
AuthorsRidel, D. A., Shinzato, P. Y., Wolf, D. F.
Conference Name2015 12th Latin American Robotic Symposium and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR)
Date Publishedoct
Keywordsautonomous navigation, Benchmark testing, Cameras, clustering, Clustering algorithms, clustering-based obstacle segmentation approach, collision avoidance, collision prevention, disparity map, hand-labeled image evaluation, Image edge detection, image segmentation, KITTI object detection benchmark, object detection, obstacle detection, precision metric, pubcrawl170111, recall metric, robots, Sensors, stereo image processing, stereo vision, urban environment

The detection of obstacles is a fundamental issue in autonomous navigation, as it is the main key for collision prevention. This paper presents a method for the segmentation of general obstacles by stereo vision with no need of dense disparity maps or assumptions about the scenario. A sparse set of points is selected according to a local spatial condition and then clustered in function of its neighborhood, disparity values and a cost associated with the possibility of each point being part of an obstacle. The method was evaluated in hand-labeled images from KITTI object detection benchmark and the precision and recall metrics were calculated. The quantitative and qualitative results showed satisfactory in scenarios with different types of objects.

Citation Keyridel_clustering-based_2015