FORM: Fixed-Lag Odometry with Reparative Mapping utilizing Rotating LiDAR Sensors

2025-10-10

AI Summary

Existing LiDAR odometry (LO) methods suffer from poor real-time performance due to multi-frame matching and accumulate pose errors and trajectory jitter owing to fixed-submap-based single-state estimation. To address these issues, this paper proposes a real-time pose estimation algorithm integrating fixed-lag smoothing (FLS) with corrective mapping. Our method operates within a single-scan-matching framework and introduces a dynamic corrective mapping mechanism that rectifies pose errors in the local map in real time. Concurrently, we construct a densely connected factor graph optimized via FLS to ensure global consistency. This design achieves both real-time processing—processing one frame at a time—and robust global optimization. Evaluated on multiple public benchmarks, our approach significantly outperforms state-of-the-art methods, delivering high-accuracy, low-jitter, and real-time trajectory estimation.

Technology Category

Intelligent Robots: State EstimationPlanning, Routing, and Scheduling: Replanning and Plan RepairSearch and Optimization: Learning to Search

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSystems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Light Detection and Ranging (LiDAR) sensors have become a de-facto sensor for many robot state estimation tasks, spurring development of many LiDAR Odometry (LO) methods in recent years. While some smoothing-based LO methods have been proposed, most require matching against multiple scans, resulting in sub-real-time performance. Due to this, most prior works estimate a single state at a time and are ``submap''-based. This architecture propagates any error in pose estimation to the fixed submap and can cause jittery trajectories and degrade future registrations. We propose Fixed-Lag Odometry with Reparative Mapping (FORM), a LO method that performs smoothing over a densely connected factor graph while utilizing a single iterative map for matching. This allows for both real-time performance and active correction of the local map as pose estimates are further refined. We evaluate on a wide variety of datasets to show that FORM is robust, accurate, real-time, and provides smooth trajectory estimates when compared to prior state-of-the-art LO methods.

Problem

• Addressing submap error propagation in LiDAR odometry systems
• Achieving real-time performance while maintaining smooth trajectories
• Correcting local map errors through active pose refinement

Innovation

• Fixed-lag smoothing over dense factor graph
• Single iterative map for matching scans
• Active correction of local map during refinement