Minimal Solvers for Indoor UAV Positioning

• URL: http://arxiv.org/abs/2003.07111v1
• Date: Mon, 16 Mar 2020 11:07:38 GMT
• Title: Minimal Solvers for Indoor UAV Positioning
• Authors: Marcus Valtonen \"Ornhag and Patrik Persson and M{\aa}rten Wadenb\"ack and Kalle {\AA}str\"om and Anders Heyden
• Abstract summary: We consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios. We show that the proposed solvers enjoy better numerical stability, are faster, and require fewer point correspondences, compared to state-of-the-art solvers.
• Score: 1.7149364927872015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation. We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these solvers do not yield adequate reconstructions for real scenes, nor do they perform fast enough to be incorporated in real-time systems. We show that the proposed solvers enjoy better numerical stability, are faster, and require fewer point correspondences, compared to state-of-the-art solvers. These properties are vital components for robust navigation in real-time systems, and we demonstrate on both synthetic and real data that our method outperforms other methods, and yields superior motion estimation.

Related papers

• Pose Estimation from Camera Images for Underwater Inspection [0.0]
  Visual localization is a cost-effective alternative to inertial navigation systems. We show that machine learning-based pose estimation from images shows promise in underwater environments. We employ novel view synthesis models to generate augmented training data, significantly enhancing pose estimation in unexplored regions.
  arXiv  Detail & Related papers  (2024-07-24T03:00:53Z)
• Simultaneous Map and Object Reconstruction [66.66729715211642]
  We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. We take inspiration from recent novel view synthesis methods and pose the reconstruction problem as a global optimization. By careful modeling of continuous-time motion, our reconstructions can compensate for the rolling shutter effects of rotating LiDAR sensors.
  arXiv  Detail & Related papers  (2024-06-19T23:53:31Z)
• Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
  Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
  arXiv  Detail & Related papers  (2022-05-16T12:14:42Z)
• Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU [58.720142291102135]
  In this paper we propose a pose estimation software exploiting neural network architectures. We show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space.
  arXiv  Detail & Related papers  (2022-04-07T08:53:18Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects [2.861848675707602]
  We present a multi-object 6D detection and tracking pipeline for potentially similar and non-textured objects. A new network architecture, trained solely with synthetic images, allows simultaneous pose estimation of multiple objects. We show how the system can be used in a real AR assistance application within the field of construction.
  arXiv  Detail & Related papers  (2022-01-31T14:36:57Z)
• Trust Your IMU: Consequences of Ignoring the IMU Drift [2.253916533377465]
  We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile. We show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups. We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications.
  arXiv  Detail & Related papers  (2021-03-15T11:24:54Z)
• FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flow [3.326320568999945]
  This study proposes the FAst ITerative Half-plane (FAITH) method to determine the course of a micro air vehicle (MAV) Results show that the computational efficiency of our solution outperforms state-of-the-art methods while keeping a high level of accuracy.
  arXiv  Detail & Related papers  (2021-02-25T12:49:02Z)
• Object-based Illumination Estimation with Rendering-aware Neural Networks [56.01734918693844]
  We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. With the estimated lighting, virtual objects can be rendered in AR scenarios with shading that is consistent to the real scene.
  arXiv  Detail & Related papers  (2020-08-06T08:23:19Z)
• DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching [47.72468932196169]
  We present the lightweight pipeline DynaMiTe, which is agnostic to the descriptor input and leverages spatial-temporal cues with efficient statistical measures. DynaMiTe achieves superior results both in terms of matching accuracy and camera pose estimation with high frame rates, outperforming state-of-the-art matching methods.
  arXiv  Detail & Related papers  (2020-07-31T12:18:18Z)

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.