Adaptive Robust Kernels for Non-Linear Least Squares Problems

Understand

State estimation is a key ingredient in most robotic systems.

• Often, state estimation is performed using some form of least squares minimization.
• Basically, all error minimization procedures that work on real-world data use robust kernels as the standard way for dealing with outliers in the data.
• These kernels, however, are often hand-picked, sometimes in different combinations, and their parameters need to be tuned manually for a particular problem.