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

• URL: http://arxiv.org/abs/2108.05560v1
• Date: Thu, 12 Aug 2021 06:52:10 GMT
• Title: Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor
• Authors: Hyungtae Lim, Minho Oh, Hyun Myung
• Abstract summary: Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition. This paper presents a novel ground segmentation method called textitPatchwork, which is robust for addressing the under-segmentation problem.
• Score: 0.1657441317977376
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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

Related papers

• GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation [0.7373617024876724]
  We propose our system GroundGrid which relies on 2D elevation maps to solve the terrain estimation and point cloud ground segmentation problems. We evaluate the ground segmentation and terrain estimation performance of GroundGrid and compare it to other state-of-the-art methods. The results show that GroundGrid is capable of outperforming other state-of-the-art systems with an average IoU of 94.78% while maintaining a high run-time performance of 171Hz.
  arXiv  Detail & Related papers  (2024-05-24T16:02:44Z)
• A Ground Segmentation Method Based on Point Cloud Map for Unstructured Roads [10.367730390937622]
  A ground segmentation method based on point cloud map is proposed, which involves three parts: region of interest extraction, point cloud registration and background subtraction. Experimental results show that the correct segmentation rate of ground points is 99.95%, and the running time is 26ms. The proposed method is practically applied to unstructured road scenarios represented by open pit mines.
  arXiv  Detail & Related papers  (2023-09-15T05:23:16Z)
• GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation [73.77505964222632]
  We present a learning-based method, namely GeoUDF, to tackle the problem of reconstructing a discrete surface from a sparse point cloud. To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation. To extract triangle meshes from the predicted UDF, we propose a customized edge-based marching cube module.
  arXiv  Detail & Related papers  (2022-11-30T06:02:01Z)
• Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection [92.75961303269548]
  The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD) We propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go. Our GPENet can outperform other methods and achieve state-of-the-art performance, well demonstrating the effectiveness and the superiority of the proposed approach.
  arXiv  Detail & Related papers  (2022-11-03T02:21:35Z)
• Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud [7.111443975103329]
  Some ground segmentation methods require fine-tuning of parameters depending on the surroundings. A partial under-segmentation problem can still emerge, which implies ground segmentation failures in some regions. We present reflected noise removal (RNR) to eliminate virtual noise points efficiently based on the 3D LiDAR model.
  arXiv  Detail & Related papers  (2022-07-25T06:09:02Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)
• On Enhancing Ground Surface Detection from Sparse Lidar Point Cloud [6.577622354490276]
  This paper proposes ground detection techniques applicable to much sparser point cloud captured by lidars with low beam resolution. The approach is based on the RANSAC scheme of plane fitting.
  arXiv  Detail & Related papers  (2021-05-25T03:58:18Z)
• PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation [96.76882806139251]
  We propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-03-11T09:42:32Z)
• A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field [0.32546166337127946]
  A fast point cloud ground segmentation approach based on a coarse-to-fine Markov random field (MRF) method is proposed. Experiments on datasets showed that our method improves on other algorithms in terms of ground segmentation accuracy.
  arXiv  Detail & Related papers  (2020-11-26T06:07:24Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)
• Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps [78.2581910688094]
  This work introduces a novel self-enhancement method to harvest accurate object localization maps and object boundaries with only category labels as supervision. In particular, the proposed Self-Enhancement Maps achieve the state-of-the-art localization accuracy of 54.88% on ILSVRC.
  arXiv  Detail & Related papers  (2020-06-09T12:35:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.