In this paper we derive theoretical results for the problem of on-line sensor calibration for a mobile robot. We consider the case of the odometry sensor. A first series of results regards the problem of understanding if a given system (consisting of a robot with several sensors) contains the necessary information to perform the on-line self calibration of one of its sensors. We consider several cases corresponding to different odometry systems and different types of robot sensors. Finally, we also consider the problem of maximizing the calibration accuracy and we formulate this problem as an optimal control problem. For the special case of a holonomic vehicle, we derive an analytical solution, i.e. we find the best trajectory which maximizes the calibration accuracy

This paper presents an improved feature-based 3D SLAM approach for a mobile robot equipped with a rotating laser scanner. The features are represented using the SPmodel, with associated planar segment information based on decimated polygon sets. An extended Kalman filter is used to build a three-dimensional map of the environment and track the robot's pose. As shown, the resulting maps are highly detailed, useful for higher-level robotic tasks and small in size

This paper presents the extension and experimental validation of the widely used EKF-based SLAM algorithm to 3D space. It uses planar features extracted probabilistically from dense three-dimensional point clouds generated by a rotating 2D laser scanner. These features are represented in compliance with the symmetries and perturbation model (SPmodel) in a stochastic map. As the robot moves, this map is updated incrementally while its pose is tracked by using an extended Kalman filter. After showing how three-dimensional data can be generated, the probabilistic feature extraction method is described, capable of robustly extracting (infinite) planes from structured environments. The SLAM algorithm is then used to track a robot moving through an indoor environment and its capabilities in terms of 3D reconstruction are analyzed.

This work presents a method for probabilistic plane fitting and an application to robotic 3D mapping. The plane is fitted in an orthogonal least-square sense and the output complies with the conventions of the symmetries and perturbation model (SPmodel). In the second part of the paper, the presented plane fitting method is used within a 3D mapping application. It is shown that by using probabilistic information, high precision 3D maps can be generated.

This paper relates first experiences using a state-of-the-art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor features a frame rate of up to 30 frames per second. Its capabilities in terms of basic obstacle avoidance and local path-planning are evaluated and compared to the performance of a standard laser scanner.