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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.