MSCKF-DVIO: Multi-State Constraint Kalman Filter based RGB-D Visual-Inertial Odometry with Spline Interpolation and Nonholonomic Constraint
Kwangyik Jung, Jae-Bong Song, Samwoo Seong, Hyun Myung · 2024
This study presents MSCKF-DVIO (Multi-State Constraint Kalman Filter - Depth-aided Visual Inertial Odom-etry) as an innovative approach to address the limitations of existing methods. MSCKF-DVIO leverages RGB-D images and low-cost IMU measurements to enhance the accuracy and efficiency of visual-inertial odometry systems. The proposed framework jointly optimizes RGB-D images and low-cost IMU measurements, enabling robust and precise state estimation. By reducing the number of state variables compared to the existing MSCKF-VIO, the efficiency of state augmentation and covariance computations is significantly enhanced. Notably, the prediction of the future state is achieved through the interpolation of past keyframe viewpoint poses from the image and short-time IMU integration. The proposed MSCKF-DVIO improves the accuracy of estimation by applying nonholonomic constraints (NHCs) of a differential-wheeled mobile robot and utilizing the zero velocity update (ZUPT) based on stationary state determination. The efficacy of MSCKF -DVIO is evaluated through the Absolute Pose Error (APE) using 3D LiDAR-based localization as the ground truth. The evaluation includes pose estimation outcomes and performance evaluations in comparison to other algorithms. The results demonstrate significant promise of MSCKF-DVIO for improving the performance and reliability of visual-inertial odometry systems.