Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud (2207.11919v2)

Abstract: In the field of 3D perception using 3D LiDAR sensors, ground segmentation is an essential task for various purposes, such as traversable area detection and object recognition. Under these circumstances, several ground segmentation methods have been proposed. However, some limitations are still encountered. First, some ground segmentation methods require fine-tuning of parameters depending on the surroundings, which is excessively laborious and time-consuming. Moreover, even if the parameters are well adjusted, a partial under-segmentation problem can still emerge, which implies ground segmentation failures in some regions. Finally, ground segmentation methods typically fail to estimate an appropriate ground plane when the ground is above another structure, such as a retaining wall. To address these problems, we propose a robust ground segmentation method called Patchwork++, an extension of Patchwork. Patchwork++ exploits adaptive ground likelihood estimation (A-GLE) to calculate appropriate parameters adaptively based on the previous ground segmentation results. Moreover, temporal ground revert (TGR) alleviates a partial under-segmentation problem by using the temporary ground property. Also, region-wise vertical plane fitting (R-VPF) is introduced to segment the ground plane properly even if the ground is elevated with different layers. Finally, we present reflected noise removal (RNR) to eliminate virtual noise points efficiently based on the 3D LiDAR reflection model. We demonstrate the qualitative and quantitative evaluations using a SemanticKITTI dataset. Our code is available at https://github.com/url-kaist/patchwork-plusplus

• The paper introduces adaptive ground likelihood estimation and noise removal, significantly reducing manual tuning in 3D LiDAR segmentation.
• It employs Temporal Ground Revert and Region-wise Vertical Plane Fitting to correct misclassifications and improve segmentation accuracy.
• Experimental results on the SemanticKITTI dataset demonstrate an impressive F1 score of 96.51%, outperforming previous methods.

Patchwork++: Fast and Robust Ground Segmentation

Introduction

The research paper introduces Patchwork++, an advanced ground segmentation method designed to address existing limitations in 3D LiDAR-based perception. While traditional methods struggle with adaptability and robustness due to requiring extensive parameter tuning and facing under-segmentation issues, Patchwork++ innovatively increases the efficiency and accuracy of ground segmentation tasks by incorporating adaptive mechanisms and novel rejection modules.

Technical Contributions

Patchwork++ is presented as an enhancement over the existing Patchwork method by introducing several key components:

1. Adaptive Ground Likelihood Estimation (A-GLE): This mechanism adaptively adjusts segmentation parameters based on historical performance data, reducing the need for manual tuning and enhancing adaptability to varying environments.
2. Temporal Ground Revert (TGR): TGR functions as a corrective layer by re-evaluating previously processed frames to detect segments that may have been improperly classified due to temporary parameter anomalies.
3. Region-wise Vertical Plane Fitting (R-VPF): This module improves upon conventional region-based segmentation by accurately estimating ground planes even when vertical structures pose identification challenges.
4. Reflected Noise Removal (RNR): Leveraging the reflective characteristics inherent in LiDAR data, RNR effectively filters out virtual noise segments that can disrupt plane estimation processes.

Experimental Results

Utilizing the SemanticKITTI dataset, Patchwork++ demonstrated superior performance metrics in terms of precision, recall, and $F_1$ scores compared to other state-of-the-art methodologies, such as RANSAC and GPF-based approaches. The implementation of R-VPF and RNR resulted in significant reductions in false positives and false negatives respectively, particularly in complex urban environments. Notably, Patchwork++ achieved an impressive $F_1$ score of 96.51%, outperforming the original Patchwork algorithm.

Implications and Future Directions

The Patchwork++ framework establishes a robust and scalable solution for ground segmentation, proving particularly beneficial in real-time applications where adaptability and speed are critical. The autonomous update features, coupled with its efficiency demonstrated in high-speed performance, make it particularly attractive for integration into autonomous vehicle systems and robotics.

Furthermore, the implications of this work extend into areas such as dynamic object recognition and LiDAR-based SLAM systems where ground segmentation acts as an essential preprocessing step. Patchwork++’s ability to maintain reliability across varied datasets paves the way for future applications in diverse environments without the need for extensive reengineering.

Conclusion

Patchwork++ enhances ground segmentation processes by tackling critical issues inherent in prior methods, primarily through its adaptive framework and advanced noise handling capabilities. This research represents significant progress in the field of 3D LiDAR perception and underscores the potential for future developments employing similar adaptive techniques in AI-driven applications. Future research might explore further optimization of the algorithm, integration with other sensor modalities, and deployment in real-world scenarios to validate and extend its practical usefulness.