SLAM

Simultaneous Location and Mapping



Estimate of unknown \(\mu\)

Classic robotics problem: reconstruct a scene by moving a camera about the room.

In practice, often boils down to a least squares inference problem, or more generally a Gaussian Belief propagation inference problem.

NICE-SLAM

I am interested in a recent cool trick that combines implicit representation with SLAM (Zhu et al. 2022).

There are a lot of cool tricks there — differentiable rendering. Hierarchical implicit representations.

References

Davison, Andrew J., and Joseph Ortiz. 2019. FutureMapping 2: Gaussian Belief Propagation for Spatial AI.” arXiv:1910.14139 [Cs], October.
Eustice, Ryan M., Hanumant Singh, and John J. Leonard. 2006. Exactly Sparse Delayed-State Filters for View-Based SLAM.” IEEE Transactions on Robotics 22 (6): 1100–1114.
Jatavallabhula, Krishna Murthy, Ganesh Iyer, and Liam Paull. 2020. ∇SLAM: Dense SLAM Meets Automatic Differentiation.” In 2020 IEEE International Conference on Robotics and Automation (ICRA), 2130–37. Paris, France: IEEE.
Zhu, Zihan, Songyou Peng, Viktor Larsson, and Weiwei Xu. 2022. “NICE-SLAM: Neural Implicit Scalable Encoding for SLAM,” 17.

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