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Localization and SLAM

Modified 2018-08-31 by Josh Roy


Modified 2019-09-22 by Andrea Censi

Having a good estimate of position is necessary for most tasks in autonomous mobile robotics. A self driving car, a delivery drone, or even a Roomba is not very useful without knowledge of its own location. The task of determining the location of a robot is known as localization. In this project, we will implement two algorithms for localization on the PiDrone: Monte Carlo localization and FastSLAM.

These algorithms cover two important cases: one in which the robot has a map of its environment available beforehand, and a second in which it does not. In this second case, the robot must use its sensors to simultaneously develop a map of its surroundings and localize itself relative to that map. Not surprisingly, this is referred to as the simultaneous localization and mapping problem, hereafter referred to as SLAM.

Github Repository

Modified 2019-12-03 by IfrahIdrees

Please use this link to generate your Github classroom repository and pull the stencil code. Use the Github repo created to handin your assignment and backup any changes you make.