Pose Estimation for Vehicle-mounted Cameras via Horizontal and Vertical Planes

• URL: http://arxiv.org/abs/2008.05743v1
• Date: Thu, 13 Aug 2020 08:01:48 GMT
• Title: Pose Estimation for Vehicle-mounted Cameras via Horizontal and Vertical Planes
• Authors: Istan Gergo Gal, Daniel Barath, Levente Hajder
• Abstract summary: We propose two novel solvers for estimating the egomotion of a calibrated camera mounted to a moving vehicle from a single affine correspondence. Both methods are solved via a linear system with a small matrix coefficient, thus, being extremely efficient. They are tested on synthetic data and on publicly available real-world datasets.
• Score: 37.653076607939745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose two novel solvers for estimating the egomotion of a calibrated camera mounted to a moving vehicle from a single affine correspondence via recovering special homographies. For the first class of solvers, the sought plane is expected to be perpendicular to one of the camera axes. For the second class, the plane is orthogonal to the ground with unknown normal, e.g., it is a building facade. Both methods are solved via a linear system with a small coefficient matrix, thus, being extremely efficient. Both the minimal and over-determined cases can be solved by the proposed methods. They are tested on synthetic data and on publicly available real-world datasets. The novel methods are more accurate or comparable to the traditional algorithms and are faster when included in state of the art robust estimators.

Related papers

• Homography Guided Temporal Fusion for Road Line and Marking Segmentation [73.47092021519245]
  Road lines and markings are frequently occluded in the presence of moving vehicles, shadow, and glare. We propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy.
  arXiv  Detail & Related papers  (2024-04-11T10:26:40Z)
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction [82.72686460985297]
  We tackle the problem of estimating a Manhattan frame. We derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. We also design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization.
  arXiv  Detail & Related papers  (2023-08-21T13:03:25Z)
• Efficient Second-Order Plane Adjustment [6.510507449705342]
  This paper focuses on the problem of estimating the optimal planes and sensor poses to minimize the point-to-plane distance. We adopt the Newton's method to efficiently solve the PA problem. Empirical evaluation shows that our algorithm converges significantly faster than the widely used LM algorithm.
  arXiv  Detail & Related papers  (2022-11-21T15:06:11Z)
• 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)
• PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes [41.517947010531074]
  Multiple near frontal-parallel planes based depth estimation demonstrated impressive results in self-supervised monocular depth estimation (MDE) We propose the PlaneDepth, a novel planes based presentation, including vertical planes and ground planes. Our method can extract the ground plane in an unsupervised manner, which is important for autonomous driving.
  arXiv  Detail & Related papers  (2022-10-04T13:51:59Z)
• Space-Partitioning RANSAC [30.255457622022487]
  A new algorithm is proposed to accelerate RANSAC model quality calculations. The method is based on partitioning the joint correspondence space, e.g., 2D-2D point correspondences, into a pair of regular grids. It reduces the RANSAC run-time by 41% with provably no deterioration in the accuracy.
  arXiv  Detail & Related papers  (2021-11-24T10:10:04Z)
• Calibrated and Partially Calibrated Semi-Generalized Homographies [65.29477277713205]
  We propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments.
  arXiv  Detail & Related papers  (2021-03-11T08:56:24Z)
• Globally Optimal Relative Pose Estimation with Gravity Prior [63.74377065002315]
  Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs that can measure the gravity vector accurately. We propose a novel globally optimal solver, minimizing the algebraic error in the least-squares sense, to estimate the relative pose in the over-determined pose. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total.
  arXiv  Detail & Related papers  (2020-12-01T13:09:59Z)
• Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences [41.35179046936236]
  We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs) It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques.
  arXiv  Detail & Related papers  (2020-07-21T10:34:45Z)

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.