GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation

• URL: http://arxiv.org/abs/2405.15664v1
• Date: Fri, 24 May 2024 16:02:44 GMT
• Title: GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation
• Authors: Nicolai Steinke, Daniel Göhring, Raùl Rojas,
• Abstract summary: 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.
• Score: 0.7373617024876724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The precise point cloud ground segmentation is a crucial prerequisite of virtually all perception tasks for LiDAR sensors in autonomous vehicles. Especially the clustering and extraction of objects from a point cloud usually relies on an accurate removal of ground points. The correct estimation of the surrounding terrain is important for aspects of the drivability of a surface, path planning, and obstacle prediction. In this article, 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 using the SemanticKITTI dataset and a novel evaluation method relying on airborne LiDAR scanning. 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. The source code is available at https://github.com/dcmlr/groundgrid

Related papers

• Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting [58.45661235893729]
  One promising self-supervised task is 3D point cloud forecasting from unannotated LiDAR sequences. We show that this task requires algorithms to implicitly capture (1) sensor extrinsics (i.e., the egomotion of the autonomous vehicle), (2) sensor intrinsics (i.e., the sampling pattern specific to the particular LiDAR sensor), and (3) the shape and motion of other objects in the scene. We render point cloud data from 4D occupancy predictions given sensor extrinsics and intrinsics, allowing one to train and test occupancy algorithms with unannotated LiDAR sequences.
  arXiv  Detail & Related papers  (2023-02-25T18:12:37Z)
• Towards Accurate Ground Plane Normal Estimation from Ego-Motion [10.22470503842832]
  We introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. Our proposed method only uses odometry as input and estimates accurate ground plane normal vectors in real time.
  arXiv  Detail & Related papers  (2022-12-08T12:06:36Z)
• 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)
• Satellite Image Time Series Analysis for Big Earth Observation Data [50.591267188664666]
  This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome.
  arXiv  Detail & Related papers  (2022-04-24T15:23:25Z)
• CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data [62.96337162094726]
  It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery. We propose a contrastive learning-based method that trains on DEM and high-resolution optical imagery. In the best scenario, the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained.
  arXiv  Detail & Related papers  (2022-04-14T15:55:00Z)
• Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification [61.44538721707377]
  We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery. We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method. We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
  arXiv  Detail & Related papers  (2022-03-11T16:14:14Z)
• Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor [0.1657441317977376]
  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.
  arXiv  Detail & Related papers  (2021-08-12T06:52:10Z)
• Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry [7.169216737580712]
  We develop a lightweight scale recovery framework leveraging an accurate and robust estimation of the ground plane. Experiments on the KITTI dataset show that the proposed framework can achieve state-of-theart accuracy in terms of translation errors. Due to the light-weight design, our framework also demonstrates a high frequency of 20Hz on the dataset.
  arXiv  Detail & Related papers  (2021-01-15T07:21:24Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)

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.