Three dimensional unique identifier based automated georeferencing and coregistration of point clouds in underground environment

• URL: http://arxiv.org/abs/2102.10731v1
• Date: Mon, 22 Feb 2021 01:47:50 GMT
• Title: Three dimensional unique identifier based automated georeferencing and coregistration of point clouds in underground environment
• Authors: Sarvesh Kumar Singh, Bikram Pratap Banerjee and Simit Raval
• Abstract summary: This study aims at overcoming practical challenges in underground or indoor laser scanning. The developed approach involves automatically and uniquely identifiable three dimensional unique identifiers (3DUIDs) in laser scans and a 3D registration (3DReG) workflow. The developed 3DUID can be used in roadway profile extraction, guided automation, sensor calibration, reference targets for routine survey and deformation monitoring.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatially and geometrically accurate laser scans are essential in modelling infrastructure for applications in civil, mining and transportation. Monitoring of underground or indoor environments such as mines or tunnels is challenging due to unavailability of a sensor positioning framework, complicated structurally symmetric layouts, repetitive features and occlusions. Current practices largely include a manual selection of discernable reference points for georeferencing and coregistration purpose. This study aims at overcoming these practical challenges in underground or indoor laser scanning. The developed approach involves automatically and uniquely identifiable three dimensional unique identifiers (3DUIDs) in laser scans, and a 3D registration (3DReG) workflow. Field testing of the method in an underground tunnel has been found accurate, effective and efficient. Additionally, a method for automatically extracting roadway tunnel profile has been exhibited. The developed 3DUID can be used in roadway profile extraction, guided automation, sensor calibration, reference targets for routine survey and deformation monitoring.

Related papers

• Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving [69.20604395205248]
  We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. We introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector. We conduct extensive experiments on the KITTI, runtime, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD.
  arXiv  Detail & Related papers  (2024-03-10T10:36:32Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration [2.433860819518925]
  This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments.
  arXiv  Detail & Related papers  (2023-01-22T21:59:38Z)
• A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles [7.340017786387768]
  This paper proposes a vision-based UAV inspection framework for dynamic tunnel environments. Our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles. Our flight experiments in a real tunnel prove that our method can autonomously inspect the tunnel excavation front surface.
  arXiv  Detail & Related papers  (2023-01-20T04:42:30Z)
• ONCE-3DLanes: Building Monocular 3D Lane Detection [41.46466150783367]
  We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. By exploiting the explicit relationship between point clouds and image pixels, a dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations.
  arXiv  Detail & Related papers  (2022-04-30T16:35:25Z)
• Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems [92.26462290867963]
  Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures. We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
  arXiv  Detail & Related papers  (2021-06-28T03:56:40Z)
• Pedestrian Detection in 3D Point Clouds using Deep Neural Networks [2.6763498831034034]
  We propose a PointNet++ architecture to detect pedestrians in dense 3D point clouds. The aim is to explore the potential contribution of geometric information alone in pedestrian detection systems. We also present a semi-automatic labeling system that transfers pedestrian and non-pedestrian labels from RGB images onto the 3D domain.
  arXiv  Detail & Related papers  (2021-05-03T20:12:11Z)
• 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)
• It's All Around You: Range-Guided Cylindrical Network for 3D Object Detection [4.518012967046983]
  This work presents a novel approach for analyzing 3D data produced by 360-degree depth scanners. We introduce a novel notion of range-guided convolutions, adapting the receptive field by distance from the ego vehicle and the object's scale. Our network demonstrates powerful results on the nuScenes challenge, comparable to current state-of-the-art architectures.
  arXiv  Detail & Related papers  (2020-12-05T21:02:18Z)
• DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes [54.239416488865565]
  We propose a fast single-stage 3D object detection method for LIDAR data. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. We find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Open dataset.
  arXiv  Detail & Related papers  (2020-04-02T17:48:50Z)
• D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [51.04841465193678]
  We leverage a 3D fully convolutional network for 3D point clouds. We propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point. Our method achieves state-of-the-art results in both indoor and outdoor scenarios.
  arXiv  Detail & Related papers  (2020-03-06T12:51:09Z)

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.