LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map (2306.17436v3)

Abstract: This letter presents an accurate and robust Lidar Inertial Odometry framework. We fuse LiDAR scans with IMU data using a tightly-coupled iterative error state Kalman filter for robust and fast localization. To achieve robust correspondence matching, we represent the points as a set of Gaussian distributions and evaluate the divergence in variance for outlier rejection. Based on the fitted distributions, a new residual metric is proposed for the filter-based Lidar inertial odometry, which demonstrates an improvement from merely quantifying distance to incorporating variance disparity, further enriching the comprehensiveness and accuracy of the residual metric. Due to the strategic design of the residual metric, we propose a simple yet effective voxel-solely mapping scheme, which only necessities the maintenance of one centroid and one covariance matrix for each voxel. Experiments on different datasets demonstrate the robustness and accuracy of our framework for various data inputs and environments. To the benefit of the robotics society, we open source the code at https://github.com/Ji1Xingyu/lio_gvm.

• The paper presents a novel probabilistic framework that represents LiDAR data as Gaussian distributions to mitigate correspondence mismatches.
• It employs a voxel-based mapping technique that maintains minimal data per voxel, achieving O(1) computational complexity for real-time performance.
• Experimental results show enhanced pose estimation accuracy and robust performance under challenging sensor noise and dynamic motion conditions.

Critique and Analysis: LIO-GVM - An Accurate, Tightly-Coupled LiDAR-Inertial Odometry with Gaussian Voxel Map

The paper "LIO-GVM: An Accurate, Tightly-Coupled LiDAR-Inertial Odometry with Gaussian Voxel Map" presents a compelling methodology for improving LiDAR inertial odometry (LIO) via a probabilistic framework that leverages Gaussian voxel maps. This paper contributes to the ongoing development of concurrent estimation and mapping strategies crucial for autonomous systems. The authors propose a novel approach that strategically ties together LiDAR features using Gaussian distributions to facilitate enhanced pose estimation and mapping.

Key Methodological Innovations

The central innovation within this paper is the representation of LiDAR points as Gaussian distributions, which plays a pivotal role in mitigating correspondence mismatching – a frequent challenge noted in LIO implementations. This probabilistic model allows for the evaluation of variance divergence, aiding in the effective rejection of outliers. Another notable contribution is the formulation of a residual metric that marries distance and variance disparities, leading to a richer, more accurate filter-based odometry mechanism.

A distinguishing feature of this work is the inclusion of a simple yet performant voxel-based mapping technique. This method requires the maintenance of minimal data per voxel—a single centroid and covariance matrix—ensuring computational efficiency. This is an improvement over conventional mapping strategies that often involve computationally intensive K-nearest neighbor (k-NN) searches or hierarchical constructs. The voxel method proposed achieves an efficient $\mathcal{O}(1)$ complexity for map operations, denoting significant advancements in real-time odometry applications.

Experimental Evaluation and Results

The authors validate their framework across diverse datasets, underscoring the robustness and accuracy of LIO-GVM. The system demonstrates robust performance amid variable data inputs and varying environmental conditions. Experimental results indicate superior pose estimation accuracy when compared to state-of-the-art LIO systems, especially in sequences characterized by challenging sensor noise and varying motion dynamics.

Significant technical results, such as better handling of false positive correspondences and improved temporal performance, affirm the system's capabilities. The experiments illustrate that both correspondence matching enhancements and the revised residual metric facilitate marked accuracy improvements. Notably, even under suboptimal IMU sensor conditions, LIO-GVM maintains commendable performance levels, which highlights its resilience in real-world scenarios.

Implications and Future Prospects

Practically, the proposed framework can dramatically improve the efficiency and accuracy of autonomous and robotic systems that rely on real-time simultaneous localization and mapping (SLAM). The stylistic pairing of LiDAR point clouds with Gaussian distributions provides a powerful alternative to rigid feature-based matching, particularly in cluttered or dynamic environments.

Theoretically, this paper opens avenues for further exploration in probabilistic odometry and mapping models. The Gaussian Voxel Map approach could inspire the development of more nuanced probabilistic spatial representations, facilitating further improvements in localization techniques.

While the paper excels in addressing the problem of odometry in robotic systems, future research may focus on adaptive voxel representation strategies, especially to accommodate diverse environmental structures and dynamic data densities. Additionally, integration with additional sensor modalities and exploring multi-agent cooperation using such frameworks could yield exciting future developments.

In conclusion, LIO-GVM presents a compelling advancement in LiDAR-Inertial Odometry, employing Gaussian distributions to enhance odometry robustness and accuracy. Its contributions lie in methodological enhancements that extend the capabilities of traditional SLAM systems, with demonstrated improvements in computational efficiency and performance accuracy.