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

TL;DR

SOCC-ICP is a semantics-assisted LiDAR odometry framework that uses occupancy grids with semantic and geometric info for robust pose estimation and mapping in diverse environments.

Contribution

It introduces a unified semantic occupancy grid mapping and scan alignment method that enhances odometry accuracy and robustness, especially in dynamic or degenerate environments.

Findings

Achieves performance comparable to state-of-the-art LiDAR odometry methods.

Robust in geometrically degenerate environments without semantic cues.

Semantic integration improves accuracy when labels are available.

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.