Exploiting polar grid structure and object shadows for fast object detection in point clouds

31 January 2020 Exploiting polar grid structure and object shadows for fast object detection in point clouds

Martin Alsfasser, Jan Siegemund, Jittu Kurian, Anton Kummert

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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330G (2020) https://doi.org/10.1117/12.2557043
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Based on PointPillars[1] and SECOND[2] we propose a novel LiDAR object detection system focused on speed, while not neglecting detection performance with the help of included occupancy grid features. We achieve this by replacing the voxelgrid defined in cartesian coordinates as introduced by Zhou et al. in VoxelNet[3] by a grid in a polar coordinate system. Doing this we can significantly reduce the number of required grid cells, while still keeping a good grid resolution in areas of highest point density close to the sensor. Because of this strong reduction on resolution we have a loss in performance which we try to regain with extending the feature network by adding occupancy grid and height map features. Furthermore we integrate the ground truth augmentation introduced in SECOND[2], injecting additional ground truth objects into the limited number of point clouds to increase variance. We achieve performance close to the state of the art, while reaching inference speeds around 12ms.

Citation Download Citation

Martin Alsfasser, Jan Siegemund, Jittu Kurian, and Anton Kummert "Exploiting polar grid structure and object shadows for fast object detection in point clouds", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330G (31 January 2020); https://doi.org/10.1117/12.2557043

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