Since early in robotics the performance of odometry techniques has been of constant research for mobile robots. This is due to its direct influence on localization. The pose error grows unbounded in dead-reckoning systems and its uncertainty has negative impacts in localization and mapping (i.e. SLAM). The dead-reckoning performance in terms of residuals, i.e. the difference between the expected and the real pose state, is related to the statistical error or uncertainty in probabilistic motion models. A novel approach to model odometry errors using Gaussian processes (GPs) is presented. The methodology trains a GP on the residual between the non-linear parametric motion model and the ground truth training data. The result is a GP over odometry residuals which provides an expected value and its uncertainty in order to enhance the belief with respect to the parametric model. The localization and mapping benefits from a comprehensive GP-odometry residuals model. The approach is applied to a planetary rover in an unstructured environment. We show that our approach enhances visual SLAM by efficiently computing image frames and effectively distributing keyframes.

Sensing of environment geometry and texture is a key requirement for mobile robotic systems. In the underwater domain, difficult environmental conditions restrict the applicability of many existing methods for 3D sensing. A new method is proposed, which uses a visible laser line projected onto a monocular camera image to perform 3D scene reconstruction. The method fuses Structured Light with Structure from Motion in an integrated process, which allows for the capturing of dense 3D point clouds on moving systems in situations with low texture and minimal scene structure. The system is evaluated in three experiment scenarios and provides an average translation error of 2% in the KITTI Benchmark for monocular visual odometry and an average model error of 1:7mm when compared with a tabletop structured light system.

Evolving a monolithic solution for complex robotic problems is hard. One of the reasons for this is the difficulty of defining a global fitness function that leads to a solution with desired operating properties. The problem with a global fitness function is that it may not reward intermediate solutions that would ultimately lead to the desired operating properties. A possible way to solve such a problem is to decompose the solution space into smaller subsolutions with lower number of intrinsic dimensions. In this paper, we apply the design principles of behavior based systems to decompose a complex robot control task into subsolutions and show how to incrementally modify the fitness function that (1) results in desired operating properties as the subsolutions are learned, and (2) avoids the need to learn the coordination of behaviors separately. We demonstrate our method by learning to control a quadrocopter flying vehicle.

Suspicion of water ice deposits in the lunar south-polar region have sparked new interest into the earth's smaller companion, and robotic crater sample return missions are being considered by a number of space agencies. The difficult terrain with an inclination of over 30°, eternal darkness and temperatures of less than -173°C make this a difficult task. In this paper we present a novel, bio-inspired light-weight system design, which demonstrates a possible approach for such a mission. The robot managed to come first in the Lunar Robotic Challenge (LRC), organised by the European Space Agency (ESA) in October 2008. Using a remote operated robot, we demonstrated to climb into and out of a lunar-like crater with inclination of more than 35° on loose substrate, and performed the collection and delivery of a 100 g soil sample without the aid of external illumination.