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

Jinwen Zhu, Xudong Zhao, Fangcheng Zhu, Jun Hu, Shi Jin, Yinian Mao, Guoquan Huang

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).

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

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).
Paper Structure (19 sections, 1 theorem, 12 equations, 6 figures, 5 tables)

This paper contains 19 sections, 1 theorem, 12 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Overview
  4. Efficient MSCKF Update with Inferred Cluster-to-Plane Constraints
  5. Plane Patches in Voxels
  6. Cluster-to-Plane Measurements
  7. MSCKF Update
  8. Monte-Carlo Simulations
  9. Real-world Experiments
  10. Accuracy and Consistency Evaluation
  11. Accuracy and Robustness Analysis
  12. Consistency Analysis
  13. Computational Efficiency and Memory Usage Evaluation
  14. Runtime Analysis
  15. Memory Usage Analysis
  16. ...and 4 more sections

Key Result

Lemma 1

Performing EKF batch update with multiple point-on-plane constraints (say $j=1,\cdots,m$) eq:pt-plane associated with the $i$-th plane patch $\bm\pi^{[i]}$, is equivalent (up to noise characterization) to update with the following one cluster-to-plane measurement: where $^{L_k}\mathbf{C}_i = \mathbf L_{k}^{[i]} \mathbf L_{k}^{[i]^T}$ is the Cholesky decomposition of the local point cluster [see

Figures (6)

  • Figure 1: System diagram of the proposed MSCKF-based LIO.
  • Figure 2: Visualization of simulated indoor environment and robot motion trajectory.
  • Figure 3: Comparison of yaw estimation error and estimated 3-sigma bounds: (top) VoxelMap, (bottom) our method.
  • Figure 4: The vehicle platform equipped with a down-facing LiDAR and RTK-GPS (providing ground-truth).
  • Figure 5: Different real-world flight trajectories and scenes.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Lemma 1
  • proof