Figure 1: Estimate of unknown μ

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

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

1 NICE-SLAM

I am interested in a recent technique that combines implicit representation with SLAM ().

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

2 Tools

2.1 jaxfg

2.2 gradslam

Differentiable GPB solver ().

2.3 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.

2.4 Incoming

3 References

Davison, and Ortiz. 2019. FutureMapping 2: Gaussian Belief Propagation for Spatial AI.” arXiv:1910.14139 [Cs].
Eustice, Singh, and Leonard. 2006. Exactly Sparse Delayed-State Filters for View-Based SLAM.” IEEE Transactions on Robotics.
Jatavallabhula, Iyer, and Paull. 2020. ∇SLAM: Dense SLAM Meets Automatic Differentiation.” In 2020 IEEE International Conference on Robotics and Automation (ICRA).
Zhu, Peng, Larsson, et al. 2022. “NICE-SLAM: Neural Implicit Scalable Encoding for SLAM.”