LIO-MARS: Non-uniform Continuous-time Trajectories for Real-time LiDAR-Inertial-Odometry

2025-11-17

AI Summary

To address the need for real-time, robust LiDAR-inertial odometry (LIO) in complex environments, this paper proposes a tightly coupled LIO framework based on continuous-time B-spline trajectories. To ensure trajectory continuity and low latency, we design a non-uniform temporal node scanning-window mechanism. For efficiency and accuracy, we accelerate Gaussian mixture model (GMM) registration and covariance computation via Kronecker product decomposition, integrate unscented transform-based skew correction, and apply intra-scan piecewise motion compensation. Furthermore, IMU preintegration pseudo-measurements and relative pose soft constraints are fused into a multi-resolution surfel map joint optimization. Evaluated across handheld, ground, and aerial robot datasets, our method achieves state-of-the-art performance—improving processing speed by 3.3× while significantly enhancing robustness and real-time localization accuracy.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Autonomous robotic systems heavily rely on environment knowledge to safely navigate. For search & rescue, a flying robot requires robust real-time perception, enabled by complementary sensors. IMU data constrains acceleration and rotation, whereas LiDAR measures accurate distances around the robot. Building upon the LiDAR odometry MARS, our LiDAR-inertial odometry (LIO) jointly aligns multi-resolution surfel maps with a Gaussian mixture model (GMM) using a continuous-time B-spline trajectory. Our new scan window uses non-uniform temporal knot placement to ensure continuity over the whole trajectory without additional scan delay. Moreover, we accelerate essential covariance and GMM computations with Kronecker sums and products by a factor of 3.3. An unscented transform de-skews surfels, while a splitting into intra-scan segments facilitates motion compensation during spline optimization. Complementary soft constraints on relative poses and preintegrated IMU pseudo-measurements further improve robustness and accuracy. Extensive evaluation showcases the state-of-the-art quality of our LIO-MARS w.r.t. recent LIO systems on various handheld, ground and aerial vehicle-based datasets.

Problem

• Develops real-time LiDAR-inertial odometry for autonomous robot navigation
• Aligns multi-resolution surfel maps using continuous-time trajectory optimization
• Enhances computational efficiency and robustness for various robotic platforms

Innovation

• Continuous-time B-spline trajectory for joint alignment
• Non-uniform temporal knot placement for scan continuity
• Kronecker operations for accelerated covariance computations