3D Lidar Mapping Relative Accuracy Automatic Evaluation Algorithm

• URL: http://arxiv.org/abs/2006.00857v1
• Date: Mon, 1 Jun 2020 11:30:31 GMT
• Title: 3D Lidar Mapping Relative Accuracy Automatic Evaluation Algorithm
• Authors: Guibin Chen, Jiong Deng, Dongze Huang, Shuo Zhang
• Abstract summary: HD (High Definition) map based on 3D lidar plays a vital role in autonomous vehicle localization, planning, decision-making, perception, etc. Many 3D lidar mapping technologies related to SLAM are used in HD map construction to ensure its high accuracy. To evaluate the accuracy of 3D lidar mapping, the most common methods use ground truth of poses to calculate the error between estimated poses and ground truth. In this paper, we proposed a relative accuracy evaluation algorithm that can automatically evaluate the accuracy of HD map built by 3D lidar mapping without ground truth.
• Score: 7.4977759910523964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: HD (High Definition) map based on 3D lidar plays a vital role in autonomous vehicle localization, planning, decision-making, perception, etc. Many 3D lidar mapping technologies related to SLAM (Simultaneous Localization and Mapping) are used in HD map construction to ensure its high accuracy. To evaluate the accuracy of 3D lidar mapping, the most common methods use ground truth of poses to calculate the error between estimated poses and ground truth, however it's usually so difficult to get the ground truth of poses in the actual lidar mapping for autonomous vehicle. In this paper, we proposed a relative accuracy evaluation algorithm that can automatically evaluate the accuracy of HD map built by 3D lidar mapping without ground truth. A method for detecting the degree of ghosting in point cloud map quantitatively is designed to reflect the accuracy indirectly, which takes advantage of the principle of light traveling in a straight line and the fact that light can not penetrate opaque objects. Our experimental results confirm that the proposed evaluation algorithm can automatically and efficiently detect the bad poses whose accuracy are less than the set threshold such as 0.1m, then calculate the bad poses percentage P_bad in all estimated poses to obtain the final accuracy metric P_acc = 1 - P_bad.

Related papers

• Map-aided annotation for pole base detection [0.0]
  In this paper, a 2D HD map is used to automatically annotate pole-like features in images. In the absence of height information, the map features are represented as pole bases at the ground level. We show how an object detector can be trained to detect a pole base.
  arXiv  Detail & Related papers  (2024-03-04T09:23:11Z)
• Volumetric Semantically Consistent 3D Panoptic Mapping [77.13446499924977]
  We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating semantic 3D maps suitable for autonomous agents in unstructured environments. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics.
  arXiv  Detail & Related papers  (2023-09-26T08:03:10Z)
• Satellite Image Based Cross-view Localization for Autonomous Vehicle [59.72040418584396]
  This paper shows that by using an off-the-shelf high-definition satellite image as a ready-to-use map, we are able to achieve cross-view vehicle localization up to a satisfactory accuracy. Our method is validated on KITTI and Ford Multi-AV Seasonal datasets as ground view and Google Maps as the satellite view.
  arXiv  Detail & Related papers  (2022-07-27T13:16:39Z)
• Monocular Camera Localization for Automated Vehicles Using Image Retrieval [8.594652891734288]
  We address the problem of finding the current position and heading angle of an autonomous vehicle in real-time using a single camera. Compared to methods which require LiDARs and high definition (HD) 3D maps in real-time, the proposed approach is easily scalable and computationally efficient.
  arXiv  Detail & Related papers  (2021-09-13T20:12:42Z)
• Probabilistic and Geometric Depth: Detecting Objects in Perspective [78.00922683083776]
  3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, as an economical solution compared to conventional settings relying on binocular vision or LiDAR, has drawn increasing attention recently but still yields unsatisfactory results. This paper first presents a systematic study on this problem and observes that the current monocular 3D detection problem can be simplified as an instance depth estimation problem.
  arXiv  Detail & Related papers  (2021-07-29T16:30:33Z)
• Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
  Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
  arXiv  Detail & Related papers  (2021-07-08T15:19:36Z)
• The Effect of Ground Truth Accuracy on the Evaluation of Localization Systems [6.702815463331469]
  We present a theoretical framework for analyzing the effect of ground truth errors on the evaluation of localization systems. Based on that, we design two algorithms for computing the real algorithmic error from the validation error and marking/map ground truth errors. Our marking error algorithm matches the real error CDF within 4%, and our map error algorithm provides a more accurate estimate of the median/tail error by 150%/72% when the map is shifted by 6m.
  arXiv  Detail & Related papers  (2021-06-25T13:07:59Z)
• PLUME: Efficient 3D Object Detection from Stereo Images [95.31278688164646]
  Existing methods tackle the problem in two steps: first depth estimation is performed, a pseudo LiDAR point cloud representation is computed from the depth estimates, and then object detection is performed in 3D space. We propose a model that unifies these two tasks in the same metric space. Our approach achieves state-of-the-art performance on the challenging KITTI benchmark, with significantly reduced inference time compared with existing methods.
  arXiv  Detail & Related papers  (2021-01-17T05:11:38Z)
• 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.