Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets

• URL: http://arxiv.org/abs/2012.01993v1
• Date: Thu, 3 Dec 2020 15:11:31 GMT
• Title: Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets
• Authors: Simon T. Isele, Marcel P. Schilling, Fabian E. Klein, Sascha Saralajew, J. Marius Zoellner
• Abstract summary: We propose a cross sensor Radar Artifact Labeling Framework (RALF) for labeling sparse radar point clouds. RALF provides plausibility labels for radar raw detections, distinguishing between artifacts and targets. We validate the results by evaluating error metrics on semi-manually labeled ground truth dataset of $3.28cdot106$ points.
• Score: 2.5899040911480187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Research on localization and perception for Autonomous Driving is mainly focused on camera and LiDAR datasets, rarely on radar data. Manually labeling sparse radar point clouds is challenging. For a dataset generation, we propose the cross sensor Radar Artifact Labeling Framework (RALF). Automatically generated labels for automotive radar data help to cure radar shortcomings like artifacts for the application of artificial intelligence. RALF provides plausibility labels for radar raw detections, distinguishing between artifacts and targets. The optical evaluation backbone consists of a generalized monocular depth image estimation of surround view cameras plus LiDAR scans. Modern car sensor sets of cameras and LiDAR allow to calibrate image-based relative depth information in overlapping sensing areas. K-Nearest Neighbors matching relates the optical perception point cloud with raw radar detections. In parallel, a temporal tracking evaluation part considers the radar detections' transient behavior. Based on the distance between matches, respecting both sensor and model uncertainties, we propose a plausibility rating of every radar detection. We validate the results by evaluating error metrics on semi-manually labeled ground truth dataset of $3.28\cdot10^6$ points. Besides generating plausible radar detections, the framework enables further labeled low-level radar signal datasets for applications of perception and Autonomous Driving learning tasks.

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