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This paper aims to present a pipeline to progressively deal with the online external parameter calibration and estimator initialization of the Stereo-inertial measurement unit (IMU) system, which does not require any prior information and is suitable for system initialization in a variety of environments.

Before calibration and initialization, a modified stereo tracking method is adopted to obtain a motion pose, which provides prerequisites for the next three steps. Firstly, the authors align the pose obtained with the IMU measurements and linearly calculate the rough external parameters and gravity vector to provide initial values for the next optimization. Secondly, the authors fix the pose obtained by the vision and restore the external and inertial parameters of the system by optimizing the pre-integration of the IMU. Thirdly, the result of the previous step is used to perform visual-inertial joint optimization to further refine the external and inertial parameters.

The results of public data set experiments and actual experiments show that this method has better accuracy and robustness compared with the state of-the-art.

This method improves the accuracy of external parameters calibration and initialization and prevents the system from falling into a local minimum. Different from the traditional method of solving inertial navigation parameters separately, in this paper, all inertial navigation parameters are solved at one time, and the results of the previous step are used as the seed for the next optimization, and gradually solve the external inertial navigation parameters from coarse to fine, which avoids falling into a local minimum, reduces the number of iterations during optimization and improves the efficiency of the system.

This paper aims to propose a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on the ground using the Stereo–inertial measurement unit (IMU) system. This method reparametrizes the pose according to the motion characteristics of TWIP and considers the impact of uneven ground on vision and IMU, which is more adaptable to the real environment.

When TWIP moves, it is constrained by the ground and swings back and forth to maintain balance. Therefore, the authors parameterize the robot pose as SE(2) pose plus pitch according to the motion characteristics of TWIP. However, the authors do not omit disturbances in other directions but perform error modeling, which is integrated into the visual constraints and IMU pre-integration constraints as an error term. Finally, the authors analyze the influence of the error term on the vision and IMU constraints during the optimization process. Compared to traditional algorithms, the algorithm is simpler and better adapt to the real environment.

The results of indoor and outdoor experiments show that, for the TWIP robot, the method has better positioning accuracy and robustness compared with the state-of-the-art.

The algorithm in this paper is proposed for the localization and mapping of a TWIP robot. Different from the traditional positioning method on SE(3), this paper parameterizes the robot pose as SE(2) pose plus pitch according to the motion of TWIP and the motion disturbances in other directions are integrated into visual constraints and IMU pre-integration constraints as error terms, which simplifies the optimization parameters, better adapts to the real environment and improves the accuracy of positioning.

To realize stable and precise localization in the dynamic environments, the authors propose a fast and robust visual odometry (VO) approach with a low-cost Inertial Measurement Unit (IMU) in this study.

The proposed VO incorporates the direct method with the indirect method to track the features and to optimize the camera pose. It initializes the positions of tracked pixels with the IMU information. Besides, the tracked pixels are refined by minimizing the photometric errors. Due to the small convergence radius of the indirect method, the dynamic pixels are rejected. Subsequently, the camera pose is optimized by minimizing the reprojection errors. The frames with little dynamic information are selected to create keyframes. Finally, the local bundle adjustment is performed to refine the poses of the keyframes and the positions of 3-D points.

The proposed VO approach is evaluated experimentally in dynamic environments with various motion types, suggesting that the proposed approach achieves more accurate and stable location than the conventional approach. Moreover, the proposed VO approach works well in the environments with the motion blur.

The proposed approach fuses the indirect method and the direct method with the IMU information, which improves the localization in dynamic environments significantly.