Fig. 1. System framework

Fig. 2. Using pose slurp to eliminate the Rolling Shutter effect

Fig. 3. Visual diagram of matching two frames piont cloud using optimized ICP CUDA algorithm

Fig. 4. Factor graph in backend

Fig. 5. System appearance

Fig. 6. Distance and angle error of ICP CUDA to match point cloud pairs with 4 m distance. (a) Using degradation factor; (b) Calculating but not using degradation factor

Fig. 7. Distance and angle error of ICP CUDA to match point cloud pairs with 8 m distance. (a) Using degradation factor; (b) Calculating but not using degradation factor

Fig. 8. (a) Range map of 3 segments of unban scene data with a duration of 5 min. (b1)-(b3) Relative translation error results of 3 segments of urban scene data excluding initialization stage; (c1)-(c3) Path results, where GT is the ground truth. VLIO mode has an average value of 0.2%-0.5% which is better than VIO and LIO mode

Fig. 9. [in Chinese]

Fig. 9. (d1)−(d2) Relative translation error results of 2 segments of tunnel scene data excluding initialization stage; (e1)−(e2) Path results, VLIO mode has lower accuracy than urban scene, but is better than VIO and LIO mode; (a)−(c), (f)−(h) Corresponding images