Visible to the public Biblio

Filters: Author is Shinzato, P. Y.  [Clear All Filters]
2017-03-08
Ridel, D. A., Shinzato, P. Y., Wolf, D. F..  2015.  A Clustering-Based Obstacle Segmentation Approach for Urban Environments. 2015 12th Latin American Robotic Symposium and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR). :265–270.

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.