Abstract — Mobile robots need maps or other forms of geometric information about the environment to navigate. The mobility sensors (LADAR, stereo, etc.) on these robotic vehicles can however populate these maps only up to a distance of a few tens of meters. A navigation system has no knowledge about the world beyond this sensing horizon. As a result, path planners that rely only on this knowledge are unable to anticipate obstacles sufficiently early and have no choice but to resort to an inefficient local obstacle avoidance behavior. However, recent developments in the computer vision community allows us to collect geometric information about the environment far beyond this sensing horizon. The coarse 3D geometric estimation that can be recovered is derived from an appearance-based model. That uses a multiple-hypothesis framework to robustly estimate scene structure from a single image and estimating confidences for each geometric label. This 3D geometric estimation is used with a previously presented navigation strategy that reasons about sensor constraints and plans for measurements while navigating towards the goal. The validity of the sensing method and navigation strategy is supported by results from simulations as well as field experiments with a real robotic platform. These results also show that significant reduction in path length can be achieved by using this framework. I.

Abstract Achieving high performance autonomous navigation is a central goal of field robotics. Efficient navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When the perception problem is clearly defined, as in well structured environments, this coupling (in the form of a cost function) is also well defined. However, as environments become less structured and more difficult to interpret, more complex cost functions are required, increasing the difficulty of their design. Recently, a class of machine learning techniques has been developed that rely upon expert demonstration to develop a function mapping perceptual data to costs. These algorithms choose the cost function such that the robot’s planned behavior mimics an expert’s demonstration as closely as possible. In this work, we extend these methods to address the challenge of dynamic and incomplete online perceptual data, as well as noisy and imperfect expert demonstration. We validate our approach on a large scale outdoor robot with hundreds of kilometers of autonomous navigation through complex natural terrains. 1

The availability of road network information simplifies autonomous driving by providing useful prior information about driving environments which is valuable for planning and perception. It tells a robotic vehicle where it can drive, models of what can be expected where, and provides contextual cues that influence driving behaviors. Currently, however, road network information for driving environments is manually generated using a combination of GPS survey and aerial imagery. These techniques for converting digital imagery into road network information are labor intensive, reducing the benefit provided by digital maps. To fully exploit the benefits of digital imagery, these processes should be automated. As a step toward this goal, we present an algorithm that extracts the structure of a parking lot visible from a given aerial image. We propose a hierarchical approach to generating and evaluating candidate hypotheses. We test three different machine learning algorithms and their combinations for removing erroneous hypotheses. From the experimental results, our Markov Random Field implementation performs best in terms of false negative rate and Eigenspots performs best in terms of false positive rate.

Abstract. While a significant body of work has been done on grasping objects, there is little prior work on placing and arranging objects in the environment. In this work, we consider placing multiple objects in complex placing areas, where neither the object nor the placing area may have been seen by the robot before. Specifically, the placements should not only be stable, but should also follow human usage preferences. We present learning and inference algorithms that consider these aspects in placing. In detail, given a set of 3D scenes containing objects, our method, based on Dirichlet process mixture models, samples human poses in each scene and learns how objects relate to those human poses. Then given a new room, our algorithm is able to select meaningful human poses and use them to determine where to place new objects. We evaluate our approach on a variety of scenes in simulation, as well as on robotic experiments. 1