Adaptive occupancy grid mapping with measurement and pose uncertainty
Daniek Joubert · SUNScholar (Stellenbosch University) · 2012
In this thesis we consider the problem of building a dense and consistent map of a mobile robot's environment that is updated as the robot moves.Such maps are vital for safe and collision-free navigation.Measurements obtained from a range sensor mounted on the robot provide information on the structure of the environment, but are typically corrupted by noise.These measurements are also relative to the robot's unknown pose (location and orientation) and, in order to combine them into a world-centric map, pose estimation is necessary at every time step.A SLAM system can be used for this task.However, since landmark measurements and robot motion are inherently noisy, the pose estimates are typically characterized by uncertainty.When building a map it is essential to deal with the uncertainties in range measurements and pose estimates in a principled manner to avoid overconfidence in the map.