PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.

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A learning approach for real-time temporal scene flow estimation from LIDAR data

Summary


Arash K. Ushani, and Ryan W. Wolcott, Jeffrey M. Walls and Ryan M. Eustice, A learning approach for real-time temporal scene flow estimation from LIDAR data. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 5666-5673, Singapore, May 2017.

Abstract

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.

Bibtex entry

@INPROCEEDINGS { aushani-2017a,
    AUTHOR = { Arash K. Ushani and and Ryan W. Wolcott and Jeffrey M. Walls and Ryan M. Eustice },
    TITLE = { A learning approach for real-time temporal scene flow estimation from {LIDAR} data },
    BOOKTITLE = { Proceedings of the IEEE International Conference on Robotics and Automation },
    YEAR = { 2017 },
    MONTH = { May },
    ADDRESS = { Singapore },
    PAGES = { 5666--5673 },
}