Graph Optimality-Aware Stochastic LiDAR Bundle Adjustment with Progressive Spatial Smoothing

TL;DR

This paper introduces PSS-GOSO, a novel large-scale LiDAR bundle adjustment method that enhances robustness, efficiency, and scalability through progressive spatial smoothing and optimality-aware stochastic optimization, improving map accuracy in complex scenes.

Contribution

The paper proposes PSS-GOSO, a new framework combining spatial smoothing and graph sparsification for robust, efficient, and scalable LiDAR bundle adjustment in large-scale environments.

Findings

Outperforms existing LBA methods in diverse scenes

Produces more accurate and robust point cloud maps

Enables effective automatic last-mile delivery in complex environments

Abstract

Large-scale LiDAR Bundle Adjustment (LBA) to refine sensor orientation and point cloud accuracy simultaneously to build the navigation map is a fundamental task in logistics and robotics. Unlike pose-graph-based methods that rely solely on pairwise relationships between LiDAR frames, LBA leverages raw LiDAR correspondences to achieve more precise results, especially when initial pose estimates are unreliable for low-cost sensors. However, existing LBA methods face challenges such as simplistic planar correspondences, extensive observations, and dense normal matrices in the least-squares problem, which limit robustness, efficiency, and scalability. To address these issues, we propose a Graph Optimality-aware Stochastic Optimization scheme with Progressive Spatial Smoothing, namely PSS-GOSO, to achieve \textit{robust}, \textit{efficient}, and \textit{scalable} LBA. The Progressive Spatial Smoothing (PSS) module extracts \textit{robust} LiDAR feature association exploiting the prior structure information obtained by the polynomial smooth kernel. The Graph Optimality-aware Stochastic Optimization (GOSO) module first sparsifies the graph according to optimality for an \textit{efficient} optimization. GOSO then utilizes stochastic clustering and graph marginalization to solve the large-scale state estimation problem for a \textit{scalable} LBA. We validate PSS-GOSO across diverse scenes captured by various platforms, demonstrating its superior performance compared to existing methods. Moreover, the resulting point cloud maps are used for automatic last-mile delivery in large-scale complex scenes. The project page can be found at: \url{https://kafeiyin00.github.io/PSS-GOSO/}.