TY - JOUR
T1 - Accurate real-time visual SLAM combining building models and GPS for mobile robot
AU - Liu, Ruyu
AU - Zhang, Jianhua
AU - Chen, Shengyong
AU - Yang, Thomas
AU - Arth, Clemens
PY - 2021/4
Y1 - 2021/4
N2 - This paper presents a novel 7 DOF (i.e., orientation, translation, and scale) visual simultaneous localization and mapping (vSLAM) system for mobile robots in outdoor environments. In the front end of this vSLAM system, a fast initialization method is designed for different vSLAM backbones, which upgrades the accuracy of trajectory and reconstruction of vSLAM with an absolute scale computed from depth maps generated by building blocks. In the back end of this vSLAM, we propose a nonlinear optimization mechanism throughout which multimodal data are combined for more robust optimization. The modality of building blocks in optimization can improve the tracking accuracy and the scale estimation. By integrating the pose estimated from visual information and the position received through GPS, the optimization further alleviates the drift. The experimental results prove that the proposed method is extremely suitable for outer AR application for outdoor environments, because our method has superior initialization performance, runs in real time, and achieves real scale, higher accuracy, and robustness.
AB - This paper presents a novel 7 DOF (i.e., orientation, translation, and scale) visual simultaneous localization and mapping (vSLAM) system for mobile robots in outdoor environments. In the front end of this vSLAM system, a fast initialization method is designed for different vSLAM backbones, which upgrades the accuracy of trajectory and reconstruction of vSLAM with an absolute scale computed from depth maps generated by building blocks. In the back end of this vSLAM, we propose a nonlinear optimization mechanism throughout which multimodal data are combined for more robust optimization. The modality of building blocks in optimization can improve the tracking accuracy and the scale estimation. By integrating the pose estimated from visual information and the position received through GPS, the optimization further alleviates the drift. The experimental results prove that the proposed method is extremely suitable for outer AR application for outdoor environments, because our method has superior initialization performance, runs in real time, and achieves real scale, higher accuracy, and robustness.
KW - Building models
KW - Graph optimization
KW - Multimodal fusion
KW - Robot localization
UR - http://www.scopus.com/inward/record.url?scp=85086050687&partnerID=8YFLogxK
U2 - 10.1007/s11554-020-00989-6
DO - 10.1007/s11554-020-00989-6
M3 - Article
AN - SCOPUS:85086050687
SN - 1861-8200
VL - 18
SP - 419
EP - 429
JO - Journal of Real-time Image Processing
JF - Journal of Real-time Image Processing
IS - 2
ER -