C-ARC: Continuous-Adaptive Range Clustering for Non-Repetitive LiDAR Sensors

2026-06-17

AI Summary

Existing LiDAR clustering methods rely on structured scan lines and explicit frame boundaries, making them ill-suited for the irregular point clouds generated by non-repetitive Risley-prism LiDARs. To address this limitation, this work proposes C-ARC—the first continuous, adaptive clustering framework tailored for non-repetitive LiDAR data. C-ARC decouples high-frequency point insertion from on-demand clustering via a persistent dual-graph structure within a sliding window and introduces an adaptive range-grid resolution mechanism initialized through an exponential control loop. This approach dynamically balances grid sparsity against collision issues without requiring prior knowledge of the scanning pattern. Experiments demonstrate that C-ARC achieves real-time 20 Hz clustering on the Livox Mid-360 and reveals that unbounded grid-cell occupancy constitutes a primary bottleneck under non-repetitive, highly concentrated scanning patterns, substantially improving clustering quality over existing grid-based methods on non-repetitive data.

Abstract

Real-time LiDAR clustering identifies structures in point clouds, which is an essential prerequisite for many mobile robotics algorithms. Current methods are mostly developed for repetitive mechanical LiDAR sensors. Recently, the use of non-repetitive LiDAR sensors is strongly increasing due to their small cost and form factor. Such non-repetitive Risley prism-based sensors violate two key assumptions of repetitive mechanical sensors: structured scan lines and well-defined frame boundaries. Their Rhodonea-curve trajectories produce non-uniform point distributions, and the absence of a rotation cycle renders conventional scan line indexing inapplicable. To meet such new requirements, we developed C-ARC, a Continuous-Adaptive Range Clustering framework that maintains a persistent dual-graph over a sliding window, decoupling high-frequency point insertion from on-demand cluster retrieval. This is crucial for key functionalities like SLAM or tracking. An adaptive range grid resolution mechanism calibrates grid dimensions at initialization using an exponential control loop, balancing the sparsity-collision trade-off without prior knowledge of the scanning pattern. Implemented as an open-sourced single-threaded C++17 library, C-ARC produces real-time cluster output at 20 Hz on commodity hardware for the Livox Mid-360. Evaluation on the Livox Avia identifies unbounded cell occupancy as the primary limitation for sensors with strongly concentrated scan patterns. The adaptive resolution mechanism additionally improves clustering quality for existing grid-based methods on non-repetitive data.

Problem

• LiDAR clustering
• non-repetitive scanning
• Risley prism
• point cloud
• scan pattern

Innovation

• non-repetitive LiDAR
• adaptive grid resolution
• continuous clustering
• dual-graph representation
• Risley prism scanning