Tag: Kalman Filter

Configuration space obstacles, Dijkstra vs A* heuristics, probabilistic completeness of RRT, asymptotic optimality of RRT*, and gradient-based trajectory optimization (CHOMP).

Feature-based vs direct visual SLAM, epipolar geometry, optical flow, dense photometric error optimization, and large-scale visual bundle adjustment.

The chicken-and-egg problem, pose graph optimization, factor graphs, iSAM2 incremental updates, appearance-based loop closure, and global bundle consistency.

Bayesian filtering recursion, motion and measurement models, first-order Jacobian linearization, Gaussian covariance propagation, and Monte Carlo Localization.