Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction

Shamwell, E. Jared; Leung, Sarah; Nothwang, William D.

doi:10.1109/iros.2018.8593573

Cited by 27 publications

(26 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However assessing the usefulness for a method for localization requires evaluating its accuracy in predicting location. A common metric for that is average relative translational drift t rel [33,49] -the distance between the predicted location and the groundtruth location divided by distance traveled and averaged over the trajectory. Table 6 summarizes both metrics, demonstrating the improvements our method achieves on both.…”

Section: Odometrymentioning

confidence: 99%

“…10 Metric ATE t rel ATE t rel Zhou [50] 0.021 17.84% 0.020 37.91% GeoNet [48] 0.012 / 0.012 / Zhan [49] / 11.92% / 12.45% Mahjourian [25] 0.013 / 0.012 / Struct2depth [7] 0. Table 6: Absolute Trajectory Error (ATE) [50] and average relative translational drift (t rel ) [33] on the 09 and 10 KITTI odometry sequences. Our method with both learned and given intrinsics is compared to prior work.…”

Section: Odometrymentioning

confidence: 99%

See 1 more Smart Citation

Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras

Gordon

Jonschkowski

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

380

267

View full text Add to dashboard Cite

We present a novel 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. Similarly to prior work, our method learns by applying differentiable warping to frames and comparing the result to adjacent ones, but it provides several improvements: We address occlusions geometrically and differentiably, directly using the depth maps as predicted during training. We introduce randomized layer normalization, a novel powerful regularizer, and we account for object motion relative to the scene. To the best of our knowledge, our 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. We evaluate our results on the Cityscapes, KITTI and Eu-RoC datasets, establishing new state of the art on depth prediction and odometry, and demonstrate qualitatively that depth prediction can be learned from a collection of YouTube videos.

show abstract

Section: Odometrymentioning

confidence: 99%

Section: Odometrymentioning

confidence: 99%

Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras

Gordon

Jonschkowski

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

380

267

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, [12] is the first unsupervised VIO system. The network learns to integrate IMU measurements and generate trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to a spatial grid of pixel coordinates.…”

Section: Unsupervised Learning Methodsmentioning

confidence: 99%

“…In our experiment, sequences 00-08 are used for training and 09-10 are used for testing. Note that sequence 03 is abandoned since its IMU data is not available in KITTI Raw Data, In addition, 5% of KITTI sequences 00-08 are selected as a validation set, which is the same to [12].…”

Section: B Network Trainingmentioning

confidence: 99%

DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints

Han¹,

Lin²,

Du³

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

This paper presents an self-supervised deep learning network for monocular visual inertial odometry (named DeepVIO). DeepVIO provides absolute trajectory estimation by directly merging 2D optical flow feature (OFF) and Inertial Measurement Unit (IMU) data. Specifically, it firstly estimates the depth and dense 3D point cloud of each scene by using stereo sequences, and then obtains 3D geometric constraints including 3D optical flow and 6-DoF pose as supervisory signals. Note that such 3D optical flow shows robustness and accuracy to dynamic objects and textureless environments. In DeepVIO training, 2D optical flow network is constrained by the projection of its corresponding 3D optical flow, and LSTMstyle IMU preintegration network and the fusion network are learned by minimizing the loss functions from ego-motion constraints. Furthermore, we employ an IMU status update scheme to improve IMU pose estimation through updating the additional gyroscope and accelerometer bias. The experimental results on KITTI and EuRoC datasets show that DeepVIO outperforms state-of-the-art learning based methods in terms of accuracy and data adaptability. Compared to the traditional methods, DeepVIO reduces the impacts of inaccurate Camera-IMU calibrations, unsynchronized and missing data.

show abstract

“…Rambach [117] proposed a deep learning approach to visual-inertial camera pose estimation through a trained short-term memory model. Shamwell [118] presented an unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation.…”

Section: Slam With Deep Learningmentioning

confidence: 99%

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

et al. 2018

View full text Add to dashboard Cite

Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work.

show abstract

Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction

Cited by 27 publications

References 20 publications

Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras

Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras

DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

Contact Info

Product

Resources

About