  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.
