Deep-Geometric 6 DoF Localization from a Single Image in Topo-metric Maps

• URL: http://arxiv.org/abs/2002.01210v1
• Date: Tue, 4 Feb 2020 10:11:46 GMT
• Title: Deep-Geometric 6 DoF Localization from a Single Image in Topo-metric Maps
• Authors: Tom Roussel, Punarjay Chakravarty, Gaurav Pandey, Tinne Tuytelaars, Luc Van Eycken
• Abstract summary: We describe a Deep-Geometric Localizer that is able to estimate the full 6 Degree of Freedom (DoF) global pose of the camera from a single image. Our method divorces the mapping and the localization algorithms (stereo and mono) and allows accurate 6 DoF pose estimation in a previously mapped environment. With potential VR/AR and localization applications in single camera devices such as mobile phones and drones, our hybrid algorithm compares favourably with the fully Deep-Learning based Pose-Net.
• Score: 39.05304338751328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe a Deep-Geometric Localizer that is able to estimate the full 6 Degree of Freedom (DoF) global pose of the camera from a single image in a previously mapped environment. Our map is a topo-metric one, with discrete topological nodes whose 6 DoF poses are known. Each topo-node in our map also comprises of a set of points, whose 2D features and 3D locations are stored as part of the mapping process. For the mapping phase, we utilise a stereo camera and a regular stereo visual SLAM pipeline. During the localization phase, we take a single camera image, localize it to a topological node using Deep Learning, and use a geometric algorithm (PnP) on the matched 2D features (and their 3D positions in the topo map) to determine the full 6 DoF globally consistent pose of the camera. Our method divorces the mapping and the localization algorithms and sensors (stereo and mono), and allows accurate 6 DoF pose estimation in a previously mapped environment using a single camera. With potential VR/AR and localization applications in single camera devices such as mobile phones and drones, our hybrid algorithm compares favourably with the fully Deep-Learning based Pose-Net that regresses pose from a single image in simulated as well as real environments.

Related papers

• RelPose++: Recovering 6D Poses from Sparse-view Observations [66.6922660401558]
  We address the task of estimating 6D camera poses from sparse-view image sets (2-8 images) We build on the recent RelPose framework which learns a network that infers distributions over relative rotations over image pairs. Our final system results in large improvements in 6D pose prediction over prior art on both seen and unseen object categories.
  arXiv  Detail & Related papers  (2023-05-08T17:59:58Z)
• CyberLoc: Towards Accurate Long-term Visual Localization [14.883028430847714]
  CyberLoc is an image-based visual localization pipeline for robust and accurate long-term estimation under challenging conditions. A mapping module is applied to build accurate 3D maps of a scene, one map for each reference sequence if there exist multiple sequences under different conditions. A single-image-based localization pipeline is performed to estimate 6-DoF camera poses for each query image, one for each 3D map.
  arXiv  Detail & Related papers  (2023-01-06T06:49:36Z)
• Neural Correspondence Field for Object Pose Estimation [67.96767010122633]
  We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
  arXiv  Detail & Related papers  (2022-07-30T01:48:23Z)
• Coupled Iterative Refinement for 6D Multi-Object Pose Estimation [64.7198752089041]
  Given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy.
  arXiv  Detail & Related papers  (2022-04-26T18:00:08Z)
• TerrainMesh: Metric-Semantic Terrain Reconstruction from Aerial Images Using Joint 2D-3D Learning [20.81202315793742]
  This paper develops a joint 2D-3D learning approach to reconstruct a local metric-semantic mesh at each camera maintained by a visual odometry algorithm. The mesh can be assembled into a global environment model to capture the terrain topology and semantics during online operation.
  arXiv  Detail & Related papers  (2022-04-23T05:18:39Z)
• Deep Camera Pose Regression Using Pseudo-LiDAR [1.5959408994101303]
  We show that converting depth maps into pseudo-LiDAR signals is a better representation for camera localization tasks. We propose FusionLoc, a novel architecture that uses pseudo-LiDAR to regress a 6DOF camera pose.
  arXiv  Detail & Related papers  (2022-02-28T20:30:37Z)
• Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo [71.59494156155309]
  Existing approaches for multi-view 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views. We present our multi-view 3D pose estimation approach based on plane sweep stereo to jointly address the cross-view fusion and 3D pose reconstruction in a single shot.
  arXiv  Detail & Related papers  (2021-04-06T03:49:35Z)
• Back to the Feature: Learning Robust Camera Localization from Pixels to Pose [114.89389528198738]
  We introduce PixLoc, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. The system can localize in large environments given coarse pose priors but also improve the accuracy of sparse feature matching.
  arXiv  Detail & Related papers  (2021-03-16T17:40:12Z)
• Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning [12.741811850885309]
  This paper addresses outdoor terrain mapping using overhead images obtained from an unmanned aerial vehicle. Dense depth estimation from aerial images during flight is challenging. We develop a joint 2D-3D learning approach to reconstruct local meshes at each camera, which can be assembled into a global environment model.
  arXiv  Detail & Related papers  (2021-01-06T02:09:03Z)
• L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation with Small Datasets [0.0]
  We propose a novel approach to perform 6 DoF object pose estimation from a single RGB-D image. We adopt a hybrid pipeline in two stages: data-driven and geometric. Our approach is more robust and accurate than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-03T17:41:29Z)

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.