Navigation & SLAM
Monte Carlo Localization
Monte Carlo localization is a particle-filter approach to robot localization, introduced by Dellaert, Fox, Burgard, and Thrun in 1999, that represents the posterior over robot pose as a set of weighted samples. Each cycle propagates particles through a motion model, reweights them by the likelihood of sensor observations against a known map, and resamples. The adaptive variant AMCL, which adjusts particle count online, is the standard localizer in the ROS navigation stack.
Why it matters for physical AI
Particle filters handle the multimodal ambiguity of global localization and kidnapped-robot recovery that Gaussian filters cannot, and this algorithm still runs on most deployed indoor mobile robot fleets.
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.