SOCC-ICP: Semantics-Assisted Odometry based on Occupancy Grids and ICP

2026-05-14

AI Summary

This work addresses the performance degradation of LiDAR odometry and downstream tasks in unknown environments caused by inconsistent map representations. To this end, we propose a unified odometry framework that integrates semantic occupancy grid mapping with scan registration. Our method leverages voxel-based encoding of both geometric and semantic statistics to adaptively select between point-to-point and point-to-plane ICP strategies, while employing ray casting to remove dynamic objects. By unifying semantic occupancy grids and ICP-based odometry within a single representation for the first time, our approach eliminates redundant structures and maintains robustness even without semantic labels, achieving further accuracy gains when semantic information is available. Experimental results demonstrate state-of-the-art performance in both standard and geometrically degenerate scenarios.

Abstract

Reliable pose estimation in previously unseen environments is a fundamental capability of autonomous systems. Existing LiDAR odometry methods typically employ point-, surfel-, or NDT-based map representations, which are distinct from the semantic occupancy grids commonly used for downstream tasks such as motion planning. We introduce SOCC-ICP, a semantics-assisted odometry framework that jointly performs Semantic OCCupancy grid mapping and LiDAR scan alignment. Each map voxel encodes geometric and semantic statistics, enabling adaptive point-to-point or point-to-plane ICP based on local planarity. Further, the occupancy grid naturally filters dynamic objects through raycasting-based free-space updates. Across diverse evaluation scenarios, SOCC-ICP achieves performance competitive with state-of-the-art LiDAR odometry and remains robust in geometrically degenerate environments, even in the absence of semantic cues. When semantic labels are available, integrating them into map construction, downsampling, and correspondence weighting yields further accuracy gains. By unifying odometry and semantic occupancy grid mapping within a single representation, SOCC-ICP eliminates redundant map structures and directly provides a map suitable for downstream robotic applications.

Problem

• LiDAR odometry
• semantic occupancy grid
• pose estimation
• map representation
• autonomous systems

Innovation

• Semantic Occupancy Grid
• LiDAR Odometry
• Adaptive ICP
• Dynamic Object Filtering
• Unified Mapping Representation