Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras
Rui Wang,Martin Schworer,Daniel Cremers +2 more
- pp 3923-3931
TLDR
Stereo Direct Sparse Odometry (Stereo DSO) as discussed by the authors integrates constraints from static stereo into the bundle adjustment pipeline of temporal multi-view stereo to improve tracking accuracy and robustness.Abstract:
We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic/extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, we propose a novel approach to integrate constraints from static stereo into the bundle adjustment pipeline of temporal multi-view stereo. Real-time optimization is realized by sampling pixels uniformly from image regions with sufficient intensity gradient. Fixed-baseline stereo resolves scale drift. It also reduces the sensitivities to large optical flow and to rolling shutter effect which are known shortcomings of direct image alignment methods. Quantitative evaluation demonstrates that the proposed Stereo DSO outperforms existing state-of-the-art visual odometry methods both in terms of tracking accuracy and robustness. Moreover, our method delivers a more precise metric 3D reconstruction than previous dense/semi-dense direct approaches while providing a higher reconstruction density than feature-based methods.read more
Citations
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Proceedings ArticleDOI
Hybrid IMU-Aided Approach for Optimized Visual Odometry
Ahmed Mahmoud,Mohamed M. Atia +1 more
TL;DR: A hybrid visual-inertial odometry solution that minimizes the computation load by dividing the mission into two interchangeable stages and compared against the IMU-aided monocular solution showed accurate positioning with the advantage of less computation.
Journal ArticleDOI
Continuous-Time Stereo-Inertial Odometry
TL;DR: The presented experimental analysis records the performance of the approach against the state-of-the-art in discrete-time Simultaneous Localization And Mapping (SLAM) on established datasets, achieving competitive results, and provides a direct comparison between online discrete- and continuous-time approaches for the first time.
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Revisiting Self-Supervised Monocular Depth Estimation.
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TL;DR: In this paper, Li et al. revisited several self-supervised methods for joint learning of depth and motion, perform a comprehensive empirical study, and unveil multiple crucial insights.
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Aerial orthoimage generation for UAV remote sensing: Review
TL;DR: In this article , a comprehensive survey on UAV ortho-image generation technologies is presented, which includes 2D mosaic-based, SfM-based and SLAM-based methods.
Proceedings ArticleDOI
AGCV-LOAM: Air-Ground Cross-View based LiDAR Odometry and Mapping
TL;DR: An air-ground cross-view based LiDAR odometry and mapping method, AGCV-LOAM, which uses satellite images as prior information to mitigate the accumulated error and outperforms other baseline matching method.
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