James Diebel

TL;DR: The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.

TL;DR: It is shown that by using an MRF to generate high-resolution, low-noise range images by integrating regular camera images into the range data, this technology can substantially improve over existing range imaging technology.

TL;DR: A practical path-planning algorithm for an autonomous vehicle operating in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the robot’s sensors is described, leading to faster search and final trajectories better suited to the structure of the environment.

TL;DR: The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high-speed desert driving without manual intervention using state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning.

TL;DR: This work proposes an integrated multi-view sensor fusion approach that combines information from multiple color cameras and multiple ToF depth sensors to obtain high quality dense and detailed 3D models of scenes challenging for stereo alone, while simultaneously reducing complex noise of ToF sensors.