Semantics-Guided Moving Object Segmentation with 3D LiDAR
This work addresses moving object segmentation for autonomous driving systems, offering an incremental improvement by integrating semantic information to enhance segmentation performance.
The paper tackles moving object segmentation (MOS) from 3D LiDAR data by proposing a semantics-guided convolutional neural network that uses semantic segmentation priors and an adjacent scan association module to exploit cross-scan features, achieving improved accuracy on the SemanticKITTI MOS dataset.
Moving object segmentation (MOS) is a task to distinguish moving objects, e.g., moving vehicles and pedestrians, from the surrounding static environment. The segmentation accuracy of MOS can have an influence on odometry, map construction, and planning tasks. In this paper, we propose a semantics-guided convolutional neural network for moving object segmentation. The network takes sequential LiDAR range images as inputs. Instead of segmenting the moving objects directly, the network conducts single-scan-based semantic segmentation and multiple-scan-based moving object segmentation in turn. The semantic segmentation module provides semantic priors for the MOS module, where we propose an adjacent scan association (ASA) module to convert the semantic features of adjacent scans into the same coordinate system to fully exploit the cross-scan semantic features. Finally, by analyzing the difference between the transformed features, reliable MOS result can be obtained quickly. Experimental results on the SemanticKITTI MOS dataset proves the effectiveness of our work.