FORM: Fixed-Lag Odometry with Reparative Mapping Utilizing Rotating LiDAR Sensors
Easton Potokar, Taylor Pool, Daniel McGann, Michael Kaess
AI summary
Problem
Most real-time LiDAR odometry methods rely on filtering or static submaps that propagate registration errors, causing jittery trajectories and degraded accuracy, while existing smoothing-based approaches lack real-time performance.
Approach
The method performs fixed-lag smoothing over a densely connected factor graph and regenerates the local map using newly smoothed poses at each timestep, enabling active error correction without sacrificing speed.
Key results
- Novel real-time fixed-lag smoothing LiDAR odometry framework
- Joint planar and point feature extraction for robust geometric matching
- Ablation studies confirming the necessity of smoothing and feature diversity
- Cross-dataset validation demonstrating superior accuracy, smoother trajectories, and real-time performance over state-of-the-art methods
Why it matters
Provides a practical, CPU-efficient smoothing-based odometry solution that improves navigation reliability and control accuracy for autonomous robots operating in diverse environments.
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 trajec- tories 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.