Appearance-based mapping using minimalistic sensor models
Autonomous Robots
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This dissertation addresses the problem of simultaneous localization and mapping for miniature robots that have extremely poor odometry and sensing capabilities. Existing robotic mapping algorithms generally assume that the robots have good odometric estimates and have sensors that can return the range or bearing to landmarks in the environment. This work focuses on solutions to this problem for robots where the above assumptions do not hold. A novel method is presented for a sensor poor mobile robot to create a topological estimate of its path through an environment by using the notion of a virtual sensor that equates “place signatures” with physical locations in space. The method is applicable in the presence of extremely poor odometry and does not require sensors that return spatial (range or bearing) information about the environment. Without sensor updates, the robot's path estimate will degrade due to the odometric errors in its position estimates. When the robot re-visits a location, the geometry of the map can be constrained such that it corrects for the odometric error and better matches the true path. Several maximum likelihood estimators are derived using this virtual sensor methodology. The first estimator uses a physics-inspired mass and spring model to represent the uncertainties in the robot's position and motion. Errors are corrected by relaxing the spring model through numerical simulation to the state of least potential energy. The second method finds the maximum likelihood solution by linearizing a Chi-squared error function. This method has the advantage of explicitly dealing with dependencies between the robot's linear and rotational errors. Finally, the third method employs the iterated form of the Extended Kalman Filter. This method has the advantage of providing a real-time update of the robot's position where the others process all the data at once. Finally, a method is presented for dealing with multiple locations that cannot be disam-biguated because their signatures appear to be identical. In order to decide which sensor readings are associated with what positions in space, the robot's sensor readings and motion history are used to calculate a discrete probability distribution over all possible robot positions.