# 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 .

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

## Tools

Differentiable GPB solver .

### ceres solver

ceres-solver, (C++), the google least squares solver, seems to solve this kind of problem. I am not sure where the covariance matrices go in. I occasionally see mention of “CUDA” in the source repo so maybe it exploits GPUs these days.

## References

Davison, Andrew J., and Joseph Ortiz. 2019. arXiv:1910.14139 [Cs], October.
Eustice, Ryan M., Hanumant Singh, and John J. Leonard. 2006. IEEE Transactions on Robotics 22 (6): 1100–1114.
Jatavallabhula, Krishna Murthy, Ganesh Iyer, and Liam Paull. 2020. 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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