An Online Initialization and Self-Calibration Method for Stereo Visual-Inertial Odometry

Huang, Weibo; Liu, Hong; Wan, Weiwei

doi:10.1109/tro.2019.2959161

Cited by 40 publications

(34 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3, which consists of machine hall scene and man-made laboratory room. According to the texture, illumination, motion blur, and fast/slow motions, the sequences are classified into easy, medium, and difficult sets [33]. The binocular cameras (denoted as CAM0 and CAM1) and the IMU are logged at 20 and 200 Hz with hardware time-synchronized, respectively.…”

Section: Methodsmentioning

confidence: 99%

Fast Monocular Visual‐Inertial Initialization with an Improved Iterative Strategy

2021

View full text Add to dashboard Cite

The initialization process has a great effect on the performance of the monocular visual inertial simultaneous localization and mapping (VI-SLAM) system. The initial estimation is usually solved by least squares such as the Gauss-Newton (G-N) algorithm, but the large iteration increment might lead to the slow convergence or even divergence. In order to solve this problem, an improved iterative strategy for initial estimation is proposed. The methodology of our initialization can be divided into four steps: Firstly, the pure visual ORB-SLAM model is utilized to make all variables observable. Secondly, the IMU preintegration technology is adopted for IMU-camera frequency alignment at the same time with key frame generation. Thirdly, an improved iterative strategy which is based on the trust region is introduced for the gyroscope bias estimation as well as the gravity direction is refined. Finally, the accelerometer bias and visual scale are estimated on the basis of previous estimations. Experimental results on the public datasets show that the estimation of initial values can be converged faster, as well as the velocity and pose of sensor suite can be estimated more accurately than the original method.

show abstract

Section: Methodsmentioning

confidence: 99%

Fast Monocular Visual‐Inertial Initialization with an Improved Iterative Strategy

2021

View full text Add to dashboard Cite

show abstract

“…Schneider et al [41] introduced information-theoretic metrics to assess the information content of trajectory segments, thus allowing them to select the most informative parts from a dataset for extrinsic spatial calibration purposes. Huang et al [19] proposed an estimator to incrementally solve several linear equations to estimate the spatial parameters between an IMU and two cameras.…”

Section: Related Workmentioning

confidence: 99%

“…Although various sophisticated online approaches for either spatial calibration or temporal calibration have been proposed, drawbacks remain. Online spatial calibration methods [15][16][17][18][19] require the measurements from different sensors to be strictly synchronized, while online temporal calibration methods [20][21][22] assume the relative transformations between different sensors are prerequisites. In cases of low-cost and self-assembled devices, accurate factory calibration and hardware synchronization are not available.…”

Section: Related Workmentioning

confidence: 99%

“…The incremental estimation will be continuously performed until the estimates get converged. The convergence is judged by using the criteria proposed by [19]. The velocities of all keyframes are estimated uniformly after the parameters are converged.…”

Section: Updates Termination and Velocity Estimationmentioning

confidence: 99%

“…To overcome the disadvantages, several online methods were developed. The online methods assumed that the measurements from a camera and an IMU were well synchronized (e.g., [14][15][16][17][18][19]), or the extrinsic spatial parameter was known in advance (e.g., [20][21][22]), or both conditions were satisfied (e.g., [23][24][25][26][27][28]). In the case where both the measurements from different sensors are asynchronous and the extrinsic spatial parameter between different sensors is unknown, most of the existing methods [29][30][31][32][33][34] are designed for filter-based VIOs since they are usually built on the Multi-State Constraint Kalman Filter (MSCKF [35]) framework.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Huang

Wan

Liu

2021

Sensors

Self Cite

View full text Add to dashboard Cite

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

show abstract

Real-Time 3D Surround View System for Vehicle Based on Panoramic Stitching Image

Gasparyan

Vasilianov

2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

An Online Initialization and Self-Calibration Method for Stereo Visual-Inertial Odometry

Cited by 40 publications

References 50 publications

Fast Monocular Visual‐Inertial Initialization with an Improved Iterative Strategy

Fast Monocular Visual‐Inertial Initialization with an Improved Iterative Strategy

Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Real-Time 3D Surround View System for Vehicle Based on Panoramic Stitching Image

Contact Info

Product

Resources

About