Exploitation of Dense MLS City Maps for 3D Object Detection

Abstract

In this paper we propose a novel method for the exploitation of High Density Localization (HDL) maps obtained by Mobile Laser Scanning in order to increase the performance of state-of-the-art real time dynamic object detection (RTDOD) methods utilizing Rotating Multi-Beam (RMB) Lidar measurements. First, we align the onboard measurements to the 3D HDL map with a multimodal point cloud registration algorithm operating in the Hough space. Next we apply a grid based probabilistic step to filter out the object regions on the RMB Lidar data which were falsely predicted as dynamic objects by RTDOD, although they are part of the static background scene. On the other hand, to find objects erroneously missed by the RTDOD predictions, we implement a Markov Random Field based point level change detection approach between the map and the current onboard measurement frame. Finally, to analyse the changed but previously unclassified segments of the RMB Lidar clouds, we apply a geometric blob separation and a Support Vector Machine based classification to distinguish the different object types. Comparative tests are provided in high traffic road sections of Budapest, Hungary, and we show an improvement of $5,96\%$ in precision, $9,21\%$ in recall and $7,93\%$ in F-score metrics against the state-of-the-art RTDOD algorithm.