Map learning is a fundamental task in mobile robotics because maps are required for a series of high level applications. In this paper, we address the problem of building maps of large-scale areas like villages or small cities. We present our modified car-like robot which we use to acquire the data about the environment. We introduce our localization system which is based on an information filter and is able to merge the information obtained by different sensors. We furthermore describe out mapping technique that is able to compactly model three-dimensional scenes and allows us efficient and accurate incremental map learning. We additionally apply a global optimization techniques in order to accurately close loops in the environment. Our approach has been implemented and deeply tested on a real car equipped with a series of sensors. Experiments described in this paper illustrate the accuracy and efficiency of the presented techniques.

This paper presents a method for wheel-ground contact angle measurement and a traction control strategy minimizing slip in rough terrain. The slip minimization algorithm has been tested and compared with a standard speed control in simulation, which allows to verify the validity of the assumptions taken during the modeling phase. The simulations show clearly the advantage of torque control versus speed control. Furthermore, the proposed method has the advantage to avoid relying on complex wheel-soil interaction models, whose parameters are generally unknown in challenging terrains.

Navigating in rough terrain is a complex task that requires the robot to be considered as a holistic system. Algorithms, which don't consider the physical dimensions and capabilities of the mobile robot lead to inefficient motion and suffer from a lack of robustness. A physical model of the robot is necessary for trajectory control. In this paper, quasi-static modeling of a six-wheeled robot with a passive suspension mechanism is presented together with a method for selecting the optimal torques considering the system constraints: maximal and minimal torques, positive normal forces. The aim of this method is to limit wheel slip and to improve climbing capabilities. The modeling and the optimization are applied to the shrimp rover.

Many algorithms related to localization need good pose prediction in order to produce accurate results. This is especially the case for data association algorithms, where false feature matches can lead to the localization system failure. In rough terrain, the field of view can vary significantly between two feature extraction steps, so a good position prediction is necessary to robustly track features. This paper presents a method for combining dead reckoning sensor information in order to provide an initial estimate of the six degrees of freedom of a rough terrain rover. An inertial navigation system (INS) and the wheel encoders are used as sensory inputs. The sensor fusion scheme is based on an extended information filter (EIF) and is extensible to any kind and number of sensors. In order to test the system, the rover has been driven on different kind of obstacles while computing both pure 3D-odometric and fused INS/3D-odometry trajectories. The results show that the use of the INS significantly improves the pose prediction.

Up to recently autonomous mobile robots were mostly designed to run within an indoor, yet partly structured and flat, environment. In rough terrain many problems arise and position tracking becomes more difficult. The robot has to deal with wheel slippage and large orientation changes. In this paper we will first present the recent developments on the off-road rover Shrimp. Then a new method, called 3D-Odometry, which extends the standard 2D odometry to the 3D space will be developed. Since it accounts for transitions, the 3D-Odometry provides better position estimates. It will certainly help to go towards real 3D navigation for outdoor robots.

In this paper a perception approach allowing for high distinctiveness is presented. The method works in accordance to the fingerprint concept. Such representation allows using a very flexible matching approach based on the minimum energy algorithm. The whole extraction and matching approach is presented in details and viewed in a topological optic, where the matching result can directly be used as observation function for a topological localization approach. The experimentation section will validate the fingerprint approach and present different set of experiments in order to explain practically the choice of different types of features.

Proposes a method for creating unique identifiers, called fingerprint sequences, for visually distinct locations by recovering statistically significant features in panoramic color images. Fingerprint sequences are expressive enough for mobile robot localization, as demonstrated using a minimum energy sequence-matching algorithm that is described. Empirical results in two different places demonstrate the reliability of the system for global localization on a Nomad Scout mobile robot.