Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction
E. Jared Shamwell,Sarah Leung,William D. Nothwang +2 more
- 01 Oct 2018
- pp 2524-2531
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TL;DR: In this paper, an unsupervised deep neural network is proposed to perform visual-inertial odometry without inertial measurement unit (IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias or white noise) or extrinsic calibration between an IMU and camera.
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Abstract: Adstract- We present an unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias or white noise) or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to a spatial grid of pixel coordinates. We evaluate our network against state-of-the-art (SOA) visual-inertial odometry, visual odometry, and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI Odometry dataset [1] and demonstrate competitive odometry performance.
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Citations
Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras
Ariel Gordon,Hanhan Li,Rico Jonschkowski,Anelia Angelova +3 more
- 10 Apr 2019
TL;DR: This work is the first to learn the camera intrinsic parameters, including lens distortion, from video in an unsupervised manner, thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale.
A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives
TL;DR: This study is the first to review Visual-inertial simultaneous localization and mapping techniques from filtering-based and optimization-based perspectives and proposes future development trends and research directions for VI-SLAM.
118
•Posted Content
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
TL;DR: In this paper, a method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal, is presented.
99
•Posted Content
SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation.
Yasin Almalioglu,Mehmet Turan,Alp Eren Sari,Muhamad Risqi U. Saputra,Pedro P. B. de Gusmao,Andrew Markham,Niki Trigoni +6 more
TL;DR: Comparisons prove that SelfVIO outperforms state-of-the-art VIO approaches in terms of pose estimation and depth recovery, making it a promising approach among existing methods in the literature.
96
Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery
TL;DR: The localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes are addressed and the generalizability of the OEC modules are demonstrated.
References
Vision meets robotics: The KITTI dataset
TL;DR: A novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research, using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras and a high-precision GPS/IMU inertial navigation system.
•Proceedings Article
Spatial transformer networks
Max Jaderberg,Karen Simonyan,Andrew Zisserman,Koray Kavukcuoglu +3 more
- 07 Dec 2015
TL;DR: This work introduces a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network, and can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps.
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Raul Mur-Artal,Juan D. Tardós +1 more
TL;DR: ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities, is presented, being in most cases the most accurate SLAM solution.
5.4K
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
Raul Mur-Artal,Juan D. Tardós +1 more
TL;DR: ORB-SLAM2 as mentioned in this paper is a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities.
4.3K
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Clément Godard,Oisin Mac Aodha,Gabriel J. Brostow +2 more
- 21 Jul 2017
TL;DR: In this article, the authors propose a novel training objective that enables CNNs to learn to perform single image depth estimation, despite the absence of ground truth depth data, by generating disparity images by training their network with an image reconstruction loss.