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this video series provides an overview of the concepts related to navigation for autonomous systems. after an introduction to the challenges and requirements for autonomous navigation, the series covers localization using particle filters, slam, path planning, and extended object tracking. the series concludes with a discussion of how to measure the performance of a navigation system against ground truth scenarios.
navigation is the ability to determine your location within an environment and to be able to figure out a path that will take you to a goal. this video provides an overview of how we get a robotic vehicle to do this autonomously.
this video presents a high-level understanding of the particle filter and shows how it can be used in monte carlo localization to determine the pose of a mobile robot inside a building.
this video provides some intuition around pose graph optimization - a popular framework for solving the simultaneous localization and mapping (slam) problem in autonomous navigation.
path planning with a* and rrt | autonomous navigation, part 4 this video describes an overview of motion and path planning and covers two popular approaches for solving these problems: search-based algorithms like a* and sampling-based algorithms like rrt and rrt*.
in a lot of scenarios, there are other objects that we may need to observe and track in order to effectively navigate within an environment. this video is going to look at extended object tracking: objects that returns multiple sensor detections.
now that you understand the overall system, see how you can use the different kinds of metrics to characterize the autonomous navigation system.