Cédric Pradalier

Combined visual odometry and visual compass for off-road mobile robots localization

In this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.

Comparison of Boosting based Terrain Classification using Proprioceptive and Exteroceptive Data

The terrain classification is a very important subject to the all-terrain robotics community. The knowledge of the type of terrain allows a rover to deal with its environment more efficiently. The work presented in this paper shows that it is possible to differentiate terrains based on their aspects, using exteroceptive sensors, as well as based on their influence on the rover’s behavior, using proprioceptive sensors. Using a boosting method (AdaBoost), these two sets of classifiers are trained and applied independently. The resulting dual algorithm identifies offline the nature of the terrain on which the vehicle is virtually driving and classifies it according to categories previously labeled, such as sand or grass. Due to the good results obtained for the classification based solely on each type of sensor, this paper concludes that the correlation between data from proprioceptive and exteroceptive sensors could be used for further applications. This paper is a summarized version of the one presented at the ISER conference.

Experiments in Autonomous Earth Moving

Abstract This paper presents a technology demonstrator currently under development and describes experiments carried out to date in autonomous bulk material handling using mobile equipment. Our primary platform is a Bobcat S185 skid-steer loader instrumented with an onboard computer, a sensor suite, and a communication link that support various levels of automation, from remote control to supervised autonomy. We present the main system components and discuss the autonomous cleaning of spillage and carryback, common bulk handling task in mining, currently executed exclusively using manually and/or remotely operated loaders. The system architecture is based on Spring, a Robotics Software Framework developed by CSIRO to support rapid development of new robotic systems, distributed as an Open Source package.