Consistent and Efficient MSCKF-based LiDAR-Inertial Odometry with Inferred Cluster-to-Plane Constraints for UAVs

2026-03-13

AI Summary

This work addresses the challenges of inconsistent state estimation and low computational efficiency in LiDAR-inertial odometry for resource-constrained UAVs. By incorporating coplanarity constraints within a sliding window into the MSCKF framework, the proposed method eliminates dependence on explicit feature parameters through nullspace projection. It further integrates voxel-based parallel data association with a compact cluster-to-plane observation model, enabling lossless dimensionality reduction and efficient tightly coupled optimization without maintaining an explicit map of features. Implemented on embedded platforms such as the Jetson TX2, the approach achieves real-time performance with minimal memory footprint, while preserving high accuracy, strong robustness, and consistent state estimation even in geometrically degenerate scenarios, significantly outperforming existing methods.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Learning & Optimization for CV
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Robust and accurate navigation is critical for Unmanned Aerial Vehicles (UAVs) especially for those with stringent Size, Weight, and Power (SWaP) constraints. However, most state-of-the-art (SOTA) LiDAR-Inertial Odometry (LIO) systems still suffer from estimation inconsistency and computational bottlenecks when deployed on such platforms. To address these issues, this paper proposes a consistent and efficient tightly-coupled LIO framework tailored for UAVs. Within the efficient Multi-State Constraint Kalman Filter (MSCKF) framework, we build coplanar constraints inferred from planar features observed across a sliding window. By applying null-space projection to sliding-window coplanar constraints, we eliminate the direct dependency on feature parameters in the state vector, thereby mitigating overconfidence and improving consistency. More importantly, to further boost the efficiency, we introduce a parallel voxel-based data association and a novel compact cluster-to-plane measurement model. This compact measurement model losslessly reduces observation dimensionality and significantly accelerating the update process. Extensive evaluations demonstrate that our method outperforms most state-of-the-art (SOTA) approaches by providing a superior balance of consistency and efficiency. It exhibits improved robustness in degenerate scenarios, achieves the lowest memory usage via its map-free nature, and runs in real-time on resource-constrained embedded platforms (e.g., NVIDIA Jetson TX2).

Problem

• LiDAR-Inertial Odometry
• UAVs
• estimation inconsistency
• computational bottleneck
• SWaP constraints

Innovation

• MSCKF
• LiDAR-Inertial Odometry
• coplanar constraints
• null-space projection
• cluster-to-plane