Acta Optica Sinica, Volume. 44, Issue 11, 1112003(2024)
High-Precision Visual SLAM Method Based on Industrial Reflective Features
Fig. 6. Selection of key camera views (selected key frame is
Fig. 7. Apply global pose constraints to current frame (
Fig. 11. Ground truth and trajectory of ORB-SLAM3 algorithm obtained by natural and reflective features respectively
Fig. 13. Ground truth and four motion trajectories obtained by proposed method, PnP, and ORB-SLAM3. (a) Sequence 1; (b) sequence 2; (c) sequence 3; (d) sequence 4
Fig. 14. Absolute trajectory error distribution of four groups of data. (a) Sequence 1; (b) sequence 2; (c) sequence 3; (d) sequence 4
Fig. 15. Attitude change curves of four groups of data. (a)-(c) Rotation of sequence 1 around three axis; (d)-(f) rotation of sequence 2 around three axis; (g)-(i) rotation of sequence 3 around three axis; (j)-(l) rotation of sequence 4 around three axis
Fig. 16. Relative attitude error curves of four groups of data. (a)-(c) Rotation of sequence 1 around three axis; (d)-(f) rotation of sequence 2 around three axis; (g)-(i) rotation of sequence 3 around three axis; (j)-(l) rotation of sequence 4 around three axis
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Zhao Guo, Ze Yang, Yongjie Ren, Yanbiao Sun, Jigui Zhu. High-Precision Visual SLAM Method Based on Industrial Reflective Features[J]. Acta Optica Sinica, 2024, 44(11): 1112003
Category: Instrumentation, Measurement and Metrology
Received: Feb. 5, 2024
Accepted: Mar. 15, 2024
Published Online: Jun. 17, 2024
The Author Email: Sun Yanbiao (yanbiao.sun@tju.edu.cn)
CSTR:32393.14.AOS240611