Our Bayesian method correctly balances the probability of the patch for stereo images at different scales.
Considering these, we propose a novel approach to combine DL method with traditional feature based approach to achieve better localization with small training data.
In this paper, we present a statistical inference on the element-wise uncertainty quantification of the estimated deforming 3D shape points in the case of the exact low-rank SDP problem.
Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion.
Our SLAM system can: (1) Incrementally build a live model by progressively fusing new observations with vivid accurate texture.
To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives.
no code implementations • 13 Nov 2019 • Xuesong Shi, Dongjiang Li, Pengpeng Zhao, Qinbin Tian, Yuxin Tian, Qiwei Long, Chunhao Zhu, Jingwei Song, Fei Qiao, Le Song, Yangquan Guo, Zhigang Wang, Yimin Zhang, Baoxing Qin, Wei Yang, Fangshi Wang, Rosa H. M. Chan, Qi She
We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately.
In this paper, we propose an approach to decouple nodes of deformation graph in large scale dense deformable SLAM and keep the estimation time to be constant.
Idled CPU is used to perform ORB- SLAM for providing robust global pose.