Abstract
To deal with the low accuracy of positioning for mobile robots when only using visual sensors and an IMU, a method based on tight coupling and nonlinear optimization is proposed to obtain a high-precision visual positioning scheme by combining measured value of the preintegrated inertial measurement unit (IMU) and values of the odometer and characteristic observations. First, the preprocessing part of the observation data includes tracking of the image data and the odometer data, and preintegration of IMU data. Second, the initialization part of the above three sensors includes IMU preintegration, odometer preintegration, and gyroscope bias calculation. It also includes the alignment of speed, gravity, and scale. Finally, a local BA (bundle adjustment) joint optimization and global graph optimization are established, so as to obtain more accurate positioning results.
Highlights
Positioning technology is undoubtedly the basic premise for a wide range of applications such as robot navigation, autonomous driving, and virtual reality. e visual positioning system has won the attention of researchers because of its small size, low cost, and simple hardware setup [1,2,3,4]
The loose-coupling method is used to use the camera and other sensors as separate modules to calculate the respective pose. e fusion information methods generally include Kalman filter (KF), extended Kalman filter (EKF), and other methods, while other sensor information is used for state propagation. e tight-coupling methods are mainly divided into two kinds: EKF [13, 14] and graph-based optimization [15, 16]
A widely concerned VIO method based on EKF is the MSCKF [13] algorithm proposed by MARS Laboratory of the University of Minnesota
Summary
Positioning technology is undoubtedly the basic premise for a wide range of applications such as robot navigation, autonomous driving, and virtual reality. e visual positioning system has won the attention of researchers because of its small size, low cost, and simple hardware setup [1,2,3,4]. A widely concerned VIO method based on EKF is the MSCKF [13] algorithm proposed by MARS Laboratory of the University of Minnesota. It can marginalize old map points and postures to ensure accuracy and computational efficiency under the condition that the number of map points and postures in the state vector is constant. Ese problems will affect the positioning accuracy and robustness of mobile robots To solve these problems, a tightly coupled and optimization-based approach is used to fuse monocular vision sensor, IMU, and wheeled odometer to achieve accurate pose estimation in this paper. Accurate positioning results are obtained by minimizing the error cost function
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