Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor (2108.05560v2)

Abstract: Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition. Unfortunately, the ground is not flat, as it features steep slopes; bumpy roads; or objects, such as curbs, flower beds, and so forth. To tackle the problem, this paper presents a novel ground segmentation method called \textit{Patchwork}, which is robust for addressing the under-segmentation problem and operates at more than 40 Hz. In this paper, a point cloud is encoded into a Concentric Zone Model-based representation to assign an appropriate density of cloud points among bins in a way that is not computationally complex. This is followed by Region-wise Ground Plane Fitting, which is performed to estimate the partial ground for each bin. Finally, Ground Likelihood Estimation is introduced to dramatically reduce false positives. As experimentally verified on SemanticKITTI and rough terrain datasets, our proposed method yields promising performance compared with the state-of-the-art methods, showing faster speed compared with existing plane fitting--based methods. Code is available: https://github.com/LimHyungTae/patchwork

• The paper introduces a novel ground segmentation method that combines a concentric zone model, region-wise plane fitting, and probabilistic ground likelihood estimation to reduce under-segmentation.
• It employs a non-uniform polar grid representation that enhances spatial resolution and computational efficiency, achieving processing speeds over 40 Hz in diverse terrains.
• Experimental validation demonstrates superior precision and robustness against state-of-the-art methods, advancing autonomous navigation and situational awareness in complex environments.

Overview of "Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor"

This paper introduces a novel approach to ground segmentation using a 3D LiDAR sensor, termed Patchwork, which is designed to robustly handle the under-segmentation problem in complex environments. The authors address the challenges associated with non-flat ground surfaces characterized by steep slopes, bumpy roads, and various obstructions. The proposed method is organized into three primary components: Concentric Zone Model (CZM) for scan representation, Region-wise Ground Plane Fitting (R-GPF), and Ground Likelihood Estimation (GLE).

Methodological Contributions

Patchwork employs a CZM-based polar grid representation that partitions a 3D point cloud into zones with differing bin sizes, enhancing expressibility and computational efficiency. This approach contrasts with uniform grid methods by improving resolution where needed while addressing sparsity issues in extended ranges. The paper systematically evaluates the impact of bin size on ground plane estimation, noting that a non-uniform distribution allows for more accurate plane fitting and reduces the instances of under-segmentation.

The R-GPF component utilizes PCA over RANSAC due to its computational efficiency, iteratively refining the estimated ground points within each region. The initial seed selection process is adapted to mitigate the effects of erroneous points obtained due to LiDAR signal reflection, enhancing robustness.

GLE then serves as a probabilistic filter to refine the classification of ground and non-ground points, leveraging parameters such as uprightness, elevation, and flatness. This step is crucial in reducing false positives and reinforcing the segmentation accuracy across diverse terrains.

Experimental Validation and Results

The authors validate Patchwork’s performance against state-of-the-art methods using the SemanticKITTI and a rough terrain dataset. The experiments demonstrate superior precision and reduced recall variability, showcasing its ability to maintain segmentation integrity in challenging urban and irregular environments. Notably, Patchwork performs at speeds exceeding 40 Hz, making it suitable for real-time applications. It shows robustness in discerning ground surfaces across various inclinations and curvatures, outperforming traditional plane fitting methods like GPF and CascadedSeg.

Implications and Future Directions

The implications of this research extend to improved navigation and object recognition for mobile platforms such as autonomous vehicles and field robots. Patchwork's efficient segmentation enhances preprocessing capabilities, leading to more accurate dynamic object tracking and reduced computational overhead during subsequent processing steps. The authors suggest potential future work may involve integrating deep learning techniques to refine the ground likelihood estimation process further. Such an enhancement could lead to more precise segmentation in even more diverse and unstructured environments, potentially elevating its efficacy in real-world applications.

In conclusion, Patchwork represents a significant methodological advancement in the area of ground segmentation with 3D LiDAR. By addressing both practical and computational challenges, it opens pathways for enhanced autonomous navigation and situational awareness in complex terrains, setting the stage for future innovations in real-time ground segmentation technologies.