Online Initialization and Automatic Camera-IMU Extrinsic Calibration for Monocular Visual-Inertial SLAM

Huang, Weibo; Liu, Hong

doi:10.1109/icra.2018.8460206

Cited by 56 publications

(30 citation statements)

References 18 publications

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“…DSO [Engel et al, 2017] is a sparse direct method that performs the epipolar matching to achieve camera tracking, based on the assumption of photometric consistency. In order to increase the robustness and improve the performance, researchers exploit IMU measurements as supplemental information, hence extending VO methods to VIO methods [Bloesch et al, 2015,Huang andLiu, 2018]. OKVIS [Leutenegger et al, 2013] is a tightly-coupled method which optimizes the reprojection error and IMU error at the same time.…”

Section: Related Workmentioning

confidence: 99%

“…However, these methods are impressionable to motion blur, occlusion, and textureless regions. As a complementary sensor of visual cameras, inertial measurement unit (IMU) has been widely adopted in VIO methods [Huang and Liu, 2018, Bloesch et al, 2015, Leutenegger et al, 2013, Qin et al, 2018 for its high-frequency motion measurement and relatively low cost. The use of IMU can help to increase the robustness as well as improving the accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Monocular Visual-inertial Odometry Network

Peng

Hua

Huang

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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Recently, unsupervised methods for monocular visual odometry (VO), with no need for quantities of expensive labeled ground truth, have attracted much attention. However, these methods are inadequate for long-term odometry task, due to the inherent limitation of only using monocular visual data and the inability to handle the error accumulation problem. By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an unsupervised visual-inertial odometry framework (UnVIO) is proposed in this paper. Our method is able to predict the per-frame depth map, as well as extracting and self-adaptively fusing visual-inertial motion features from image-IMU stream to achieve long-term odometry task. A novel sliding window optimization strategy, which consists of an intra-window and an inter-window optimization, is introduced for overcoming the error accumulation and scale ambiguity problem. The intra-window optimization restrains the geometric inferences within the window through checking the photometric consistency. And the inter-window optimization checks the 3D geometric consistency and trajectory consistency among predictions of separate windows. Extensive experiments have been conducted on KITTI and Malaga datasets to demonstrate the superiority of UnVIO over other state-of-the-art VO / VIO methods. The codes are open-source.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised Monocular Visual-inertial Odometry Network

Peng

Hua

Huang

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Self Cite

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“…Directly fixing the outside nodes makes the conditioning-based method more sensitive to linearization error, so a high-precision initialization method is essential for the VINS problem [7]. Two types of online initialization methods are found in the literature: loosely-coupled methods [31,32,33] and tightly-coupled methods [34,35]. As reported by Liu et al [32], the tightly-coupled methods that aim to recover all navigation quantities in one attempt perform poorly because they attempt to solve a large number of variables in a poorly conditioned system.…”

Section: Introductionmentioning

confidence: 99%

“…As reported by Liu et al [32], the tightly-coupled methods that aim to recover all navigation quantities in one attempt perform poorly because they attempt to solve a large number of variables in a poorly conditioned system. In this study, we adopted the method proposed by Huang et al [31].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System

Jiang

Niu

Guo

et al. 2019

Sensors

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The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided inertial navigation system problem. We present a novel hybrid sliding window optimizer to achieve information fusion for a tightly-coupled vision-aided inertial navigation system. It possesses the advantages of both the conditioning-based method and the prior-based method. A novel distributed marginalization method was also designed based on the multi-state constraints method with significant efficiency improvement over the traditional method. The performance of the proposed algorithm was evaluated with the publicly available EuRoC datasets and showed competitive results compared with existing algorithms.

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“…The maturation and development of the above techniques underpin a successful robot application in the power patrol inspection. Accordingly, the efficiently initialized VIO permits the robot to perform accurate localization and navigation tasks [21,22]. Based on the above discussion, an optimized VIO system is presented to take into account the problems associated with the initialization efficiency and feature matching results.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation

Xia

Meng

Chi

et al. 2019

Sensors

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The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.

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Online Initialization and Automatic Camera-IMU Extrinsic Calibration for Monocular Visual-Inertial SLAM

Cited by 56 publications

References 18 publications

Unsupervised Monocular Visual-inertial Odometry Network

Unsupervised Monocular Visual-inertial Odometry Network

A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System

An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation

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