Welcome to the ICRA 2022 Non-Gaussian SLAM tutorial. This hands-on workshop will take you through the some of the canonical issues motivating Non-Gaussian SLAM, and demonstrate how real-world problems (like false loop-closures and distributing computations) are addressed with this emerging technique. Furthermore, the DIY examples in this tutorial will show how the same modeling philosophy can be extended to other non-Gaussian behavior, and how this new modeling freedom can simplify SLAM front-end processes.
Learn more about the four drivers or non-Gaussian behavior in SLAM.
Visitors can either read, or “bring-your-own-laptop” to try out the non-Gaussian SLAM code for real!
The examples listed below aim to elucidate new avenues in Non-Gaussian SLAM:
The following DIY canonical examples—followed by real-world use-cases—intends to guide the reader on the four main drivers of non-Gaussian behavior in SLAM. You can work through any or all of the following examples (to reinforce the insights):
The follow example shows how multi-session AprilTag detections from camera data came be joined together (with simulated ambiguity) to build a larger map.
This example is a modern reworking of the popular 3 door robot example. In this example a continuous, but non-Gaussian factor graph is constructed so as to capture the same uncertainty.
The follow code snippets show how real-world Radar data can be processed using a Non-Gaussian SLAM pipeline.
Tutorial text — work in progress.
This example showcases a very recent real-world example where data association uncertainty modeling allows for use of imperfect Navigation-Affordances: