This is basically Line of Best Fit.
Saw this in the Kalman Filter book.
Learning more seriously from Cyrill Stachniss in the context of SLAM.
Least Squares vs. SVD?
SVD (Singular Value Decomposition): A mathematical technique for factorizing a matrix into three separate matrices. Used in various applications, including dimensionality reduction, noise reduction, and solving linear equations.
Least Squares: A method for estimating the coefficients in a linear regression model. Minimizes the sum of the squared differences between observed and predicted values.
Non-Linear Least Squares
Found from a quick google search https://github.com/Rookfighter/least-squares-cpp
Options to solve:
- Ceres Solver http://ceres-solver.org/
- G2o https://github.com/RainerKuemmerle/g2o
- CuSOLVER https://docs.nvidia.com/cuda/cusolver/index.html#using-the-cusolver-api