An Improved Strategy for Active Visual Odometry Based on Robust Adaptive Unscented Kalman Filter

• Published in: IEEE transactions on industrial electronics (1982) 2024-08, Vol.71 (8), p.1-10
• Main Authors: Yuwen, Xuan, Chen, Lu, Chen, Long, Zhang, Hui
• Format: Article
• Language: English
• Subjects: Active simultaneous localization and mapping (SLAM); Cameras; Extraterrestrial measurements; Kalman filters; Motion measurement; Odometers; outlier detection and correction; robust adaptive unscented Kalman filter (RAUKF); Robustness (mathematics); Rotation; Transforms; Visual odometry; visual odometry (VO); Visualization
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2023.3323730

For active visual odometry (VO), the visual information detected by the positioning camera matters. By actively controlling the gaze of the camera, the VO tends to retrieve some effective factors, such as textured objects with rich feature points. However, the active rotation of the camera can introduce more uncertainties, which may cause additional gross errors. Therefore, it is a considerable problem for the active VO to avoid the adverse effects of the active rotation while benefiting from it. To address the issue, this article proposes an improved strategy based on a robust adaptive unscented Kalman filter (RAUKF) and the relative posture of the active camera for the active VO. The pose outputted from the VO is transformed to the vehicle pose by means of the pose of the pan-tilt, and the transformed pose is treated as the measurement of the vehicle motion. Subsequently, the measurement is fed to the RAUKF to generate a refined estimation of the vehicle pose, which is then inversely transformed to obtain a more precise camera pose. Finally, the feature points cloud of the VO can be corrected according to the refined camera pose. The proposed method effectively improves the positioning accuracy of the active VO, as demonstrated through numerical and real-vehicle tests. The relative translation error and the relative rotation error of the proposed method are 1.6% and 0.0037 deg/m in average, which reduce 96.22% and 94.79% compared with the raw outputs of the active VO.