TRI Authors: Ryan W. Wolcott, Jeffrey M. Walls, Ryan M. Eustice
All Authors: Arash K. Ushani, Ryan W. Wolcott, Jeffrey M. Walls, Ryan M. Eustice
Many autonomous systems require the ability to perceive and understand motion in a dynamic environment. We present a novel algorithm that estimates this motion from raw LIDAR data in real-time without the need for segmentation or model-based tracking. The sensor data is first used to construct an occupancy grid. The foreground is then extracted via a learned background filter. Using the filtered occupancy grid, raw scene flow between successive scans is computed. Finally, we incorporate these measurements in a filtering framework to estimate temporal scene flow. We evaluate our method on the KITTI dataset. Read More
Citation: Ushani, Arash K., Ryan W. Wolcott, Jeffrey M. Walls, and Ryan M. Eustice. "A learning approach for real-time temporal scene flow estimation from lidar data." In 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5666-5673. IEEE, 2017.