UVIO: Adaptive Kalman Filtering UWB-Aided Visual-Inertial SLAM System for Complex Indoor Environments

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 17; p. 3245
• Main Authors: Li, Junxi, Wang, Shouwen, Hao, Jiahui, Ma, Biao, Chu, Henry K.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Accuracy; adaptive Kalman filter; Algorithms; Cameras; Covariance matrix; energy; Energy consumption; factor graph optimization; Global positioning systems; GPS; immunity; Indoor environments; Inertial coordinates; Inertial platforms; Interference immunity; Kalman filters; Line of sight; Localization; Marking and tracking techniques; Methods; NLOS identification and correction; Obstructions; Pose estimation; Radio frequency identification; Robots; RSSI-EOP factor-related gain; Sensors; Signal strength; Simultaneous localization and mapping; ultra-wideband (UWB); Ultrawideband; variance covariance matrix; Visual aspects; Visual effects; Visual stimuli; Visual tasks; visual-inertia SLAM
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16173245

Precise positioning in an indoor environment is a challenging task because it is difficult to receive a strong and reliable global positioning system (GPS) signal. For existing wireless indoor positioning methods, ultra-wideband (UWB) has become more popular because of its low energy consumption and high interference immunity. Nevertheless, factors such as indoor non-line-of-sight (NLOS) obstructions can still lead to large errors or fluctuations in the measurement data. In this paper, we propose a fusion method based on ultra-wideband (UWB), inertial measurement unit (IMU), and visual simultaneous localization and mapping (V-SLAM) to achieve high accuracy and robustness in tracking a mobile robot in a complex indoor environment. Specifically, we first focus on the identification and correction between line-of-sight (LOS) and non-line-of-sight (NLOS) UWB signals. The distance evaluated from UWB is first processed by an adaptive Kalman filter with IMU signals for pose estimation, where a new noise covariance matrix using the received signal strength indicator (RSSI) and estimation of precision (EOP) is proposed to reduce the effect due to NLOS. After that, the corrected UWB estimation is tightly integrated with IMU and visual SLAM through factor graph optimization (FGO) to further refine the pose estimation. The experimental results show that, compared with single or dual positioning systems, the proposed fusion method provides significant improvements in positioning accuracy in a complex indoor environment.