DNN-Based Map Deviation Detection in LiDAR Point Clouds

Abstract

In this work we present a novel deep learning-based approach to detect and specify map deviations in erroneous or outdated high-definition (HD) maps using both sensor and map data as input to a deep neural network (DNN). We first present our proposed reference method for map deviation detection (MDD) utilizing a sensor-only DNN detecting traffic signs, traffic lights, and pole-like objects in LiDAR data, with deviations obtained by subsequently comparing detected objects and examined map. Second, we facilitate the object detection task by using the examined map as additional input to the network. Third, we employ a specialized MDD network to directly infer the correctness of the map input. Finally, we demonstrate the robustness of our approach for challenging scenes featuring occlusions and a reduced point density, e.g., due to heavy rain. Our code is available at https://github.com/Volkswagen/3dhd_devkit.

Keywords

• 3D object detection
• automated driving
• convolutional neural network (CNN)
• deep neural network (DNN)
• deviation detection
• high-definition (HD) map