Abstract
In recent years, dense visual SLAM (Simultaneous Localization And Mapping) has been proved that it has lots of advantages on the accuracy of pose estimation compared to sparse features due to exploiting the more available information in image data. In this paper, we propose a scalable dense frame-to-model SLAM system based on KinectFusion algorithm. We design an effective active volume shift strategy to extend the tracking range without increment on storage resource. Furthermore, we propose three local optimization methods including motion predicting, weighted ICP (Iterative Closest Point) and multi-modelframe combined estimation to improve the tracking stability and decrease the accumulative error. Note that, as the basis of on-line augmented reality application, our work focus on local optimization rather than global closing loop optimization which is a classical problem in SLAM. At last, we evaluate our optimized approaches on publicly available benchmark datasets, and compare it with the original KinectFusion, a similar method and another two frame-to-frame RGBD SLAM methods. The results indicate our approaches yield a certain degree improvement on the performance of tracking accuracy.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61271338, 61401390), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ14F010005), and the Open Projects Program of National Laboratory of Pattern Recognition of China (Grant No. 201306308).
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Li, JN., Wang, LH., Li, Y. et al. Local optimized and scalable frame-to-model SLAM. Multimed Tools Appl 75, 8675–8694 (2016). https://doi.org/10.1007/s11042-015-2780-5
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DOI: https://doi.org/10.1007/s11042-015-2780-5