Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation

• URL: http://arxiv.org/abs/2302.06149v1
• Date: Mon, 13 Feb 2023 07:18:24 GMT
• Title: Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation
• Authors: Binqian Jiang, Shaojie Shen
• Abstract summary: This paper proposes a simple, effective, and efficient topological loop closure detection pipeline with accurate 3-DoF metric pose estimation. We interpret the Cartesian birds' eye view (BEV) image projected from 3D LiDAR points as layered distribution of structures. A retrieval key is designed to accelerate the search of a database indexed by layered KD-trees.
• Score: 31.968749056155467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes \textit{Contour Context}, a simple, effective, and efficient topological loop closure detection pipeline with accurate 3-DoF metric pose estimation, targeting the urban utonomous driving scenario. We interpret the Cartesian birds' eye view (BEV) image projected from 3D LiDAR points as layered distribution of structures. To recover elevation information from BEVs, we slice them at different heights, and connected pixels at each level will form contours. Each contour is parameterized by abstract information, e.g., pixel count, center position, covariance, and mean height. The similarity of two BEVs is calculated in sequential discrete and continuous steps. The first step considers the geometric consensus of graph-like constellations formed by contours in particular localities. The second step models the majority of contours as a 2.5D Gaussian mixture model, which is used to calculate correlation and optimize relative transform in continuous space. A retrieval key is designed to accelerate the search of a database indexed by layered KD-trees. We validate the efficacy of our method by comparing it with recent works on public datasets.

Related papers

• Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
  Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding. An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
  arXiv  Detail & Related papers  (2024-11-25T10:14:10Z)
• CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction [2.0375637582248136]
  Cross-View Center Point-Fusion is a state-of-the-art model to perform 3D object detection. Our architecture utilizes aspects from previously established algorithms, Cross-View Transformers and CenterPoint.
  arXiv  Detail & Related papers  (2024-10-15T02:55:07Z)
• 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)
• PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction [72.75478398447396]
  We propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane.
  arXiv  Detail & Related papers  (2023-08-31T17:57:17Z)
• CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network [66.24726878647543]
  Estimating the 6-DoF pose of a rigid object from a single RGB image is a crucial yet challenging task. Recent studies have shown the great potential of dense correspondence-based solutions. We propose a novel pose estimation algorithm named CheckerPose, which improves on three main aspects.
  arXiv  Detail & Related papers  (2023-03-29T17:30:53Z)
• A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation [12.499361832561634]
  We present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation. Our method significantly outperforms existing methods on overlap prediction, especially in scenes with small overlaps.
  arXiv  Detail & Related papers  (2023-02-28T12:01:16Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Sampling [75.957103837167]
  Reconstructing a 3D shape based on a single sketch image is challenging due to the large domain gap between a sparse, irregular sketch and a regular, dense 3D shape. Existing works try to employ the global feature extracted from sketch to directly predict the 3D coordinates, but they usually suffer from losing fine details that are not faithful to the input sketch.
  arXiv  Detail & Related papers  (2022-08-14T16:37:51Z)
• Parallel Structure from Motion for UAV Images via Weighted Connected Dominating Set [5.17395782758526]
  This paper proposes an algorithm to extract the global model for cluster merging and designs a parallel SfM solution to achieve efficient and accurate UAV image orientation. The experimental results demonstrate that the proposed parallel SfM can achieve 17.4 times efficiency improvement and comparative orientation accuracy.
  arXiv  Detail & Related papers  (2022-06-23T06:53:06Z)

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.