SLAM

NICE-SLAM
Tools
References

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 technique that combines implicit representation with SLAM (Zhu et al. 2022).

There are many cool tricks combined there — differentiable rendering etc. Hierarchical implicit representations.

Tools

jaxfg

brentyi/jaxfg: Factor graphs and nonlinear optimization for JAX

gradslam

Differentiable GPB solver (Jatavallabhula, Iyer, and Paull 2020).

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.

Incoming

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.

Simultaneous Location and Mapping