Mapping with Reflection -- Detection and Utilization of Reflection in 3D Lidar Scans
This addresses the challenge of handling reflections in autonomous navigation or mapping systems, but it appears incremental as it builds on existing SLAM methods.
The paper tackles the problem of detecting and utilizing reflections in 3D Lidar scans to classify points and map objects outside the line of sight, resulting in improved map quality in a SLAM framework as demonstrated with real scan data.
This paper presents a method to detect reflection of 3D light detection and ranging (Lidar) scans and uses it to classify the points and also map objects outside the line of sight. Our software uses several approaches to analyze the point cloud, including intensity peak detection, dual return detection, plane fitting, and finding the boundaries. These approaches can classify the point cloud and detect the reflection in it. By mirroring the reflection points on the detected window pane and adding classification labels on the points, we can improve the map quality in a Simultaneous Localization and Mapping (SLAM) framework. Experiments using real scan data and ground truth data showcase the effectiveness of our method.