PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry (2206.00266v1)

Abstract: Numerous researchers have conducted studies to achieve fast and robust ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In particular, ground-optimized LiDAR odometry usually employs ground segmentation as a preprocessing method. This is because most of the points in a 3D point cloud captured by a 3D LiDAR sensor on a terrestrial platform are from the ground. However, the effect of the performance of ground segmentation on LiDAR odometry is still not closely examined. In this paper, a robust ground-optimized LiDAR odometry framework is proposed to facilitate the study to check the effect of ground segmentation on LiDAR SLAM based on the state-of-the-art (SOTA) method. By using our proposed odometry framework, it is easy and straightforward to test whether ground segmentation algorithms help extract well-described features and thus improve SLAM performance. In addition, by leveraging the SOTA ground segmentation method called Patchwork, which shows robust ground segmentation even in complex and uneven urban environments with little performance perturbation, a novel ground-optimized LiDAR odometry is proposed, called PaGO-LOAM. The methods were tested using the KITTI odometry dataset. \textit{PaGO-LOAM} shows robust and accurate performance compared with the baseline method. Our code is available at https://github.com/url-kaist/AlterGround-LeGO-LOAM.

• The paper introduces PaGO-LOAM, integrating Patchwork ground segmentation into LiDAR odometry to enhance feature extraction and mapping precision.
• It demonstrates superior performance over baseline methods, notably reducing translation and rotation errors in challenging rural terrains.
• Experiments on the KITTI dataset validate its efficacy, underscoring the role of robust preprocessing in optimizing SLAM performance.

Overview of PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

The research paper presents a novel LiDAR odometry framework, termed PaGO-LOAM, that emphasizes robust ground segmentation to enhance the performance of simultaneous localization and mapping (SLAM) processes on terrestrial mobile platforms. This paper highlights the critical role of ground segmentation in LiDAR-based SLAM and seeks to examine its effects using a state-of-the-art odometry method. The proposed framework can serve as a benchmark for understanding whether precise ground segmentation contributes significantly to SLAM performance by improving feature extraction.

Methodological Insights

The authors introduce PaGO-LOAM, which incorporates the Patchwork ground segmentation technique into a LiDAR odometry framework. Existing methods often utilize basic ground segmentation as a preprocessing step to isolate ground points in the 3D point clouds captured by LiDAR. However, these methods, such as LeGO-LOAM, have not thoroughly examined the segmentation's impact on SLAM performance. By substituting existing ground segmentation modules with Patchwork, the researchers are able to assess whether ongoing segmentation can enhance odometry precision in environments with uneven terrains, typically encountered in rural settings.

Experimental Evaluation

PaGO-LOAM was tested using the KITTI odometry dataset, with experiments focusing on both qualitative and quantitative analyses of ground segmentation and odometry performance. Ground segmentation accuracy was evaluated using precision and recall metrics, demonstrating the robustness of Patchwork in maintaining consistent ground segmentation even in complex urban areas. Furthermore, in terms of odometry, the framework's performance was assessed by measuring relative translation error (t_rel), relative rotation error (r_rel), and absolute trajectory error (ATE).

According to the experimental results, PaGO-LOAM exhibited superior performance over the baseline methods, particularly in rural scenes where the ground conditions are highly variable and challenging. The robustness of Patchwork in handling complex surfaces led to more precise odometry measurements, highlighting the value of incorporating sophisticated preprocessing steps in LiDAR-based SLAM systems.

Practical and Theoretical Implications

This research underscores the importance of preprocessing in LiDAR odometry and its potential to enhance the SLAM framework when adequately executed. It indicates that while algorithms like LeGO-LOAM provide a good baseline, state-of-the-art ground segmentation methods such as Patchwork can significantly improve performance, especially in less ideal conditions. The proposed approach opens up further research into adaptive segmentation methods that adjust preprocessing techniques dynamically based on environmental conditions to optimize SLAM performance.

Future Developments

The paper lays groundwork for the exploration of more comprehensive ground segmentation impacts on various SLAM frameworks and potential improvements in odometry precision. As mobile platforms increasingly operate in diverse terrains, developing robust, environment-aware segmentation and odometry methods will become essential. Continued research may focus on integrating machine learning algorithms to further enhance the predictive accuracy of ground segmentation and on testing the framework in broader, more varied environments to fully understand its versatility and constraints.

The introduction of PaGO-LOAM marks a significant contribution towards refining LiDAR-based navigation technologies, specifying precise modules that significantly contribute to overall system performance, and identifying potential areas for further refinement and development.