CSAIL Event Calendar

Thesis Defense: Trajectory Bundle Estimation For Perception-Driven Planning

When operating in unknown environments, autonomous vehicles must perceive and understand the environment ahead in order to make effective navigation decisions. Today, autonomous vehicles tend to rely exclusively on metric representations built using range sensors to plan paths. However, such sensors are limited by their maximum range, field of view, and occluding obstacles in the foreground, thereby limiting the effectiveness of planners that depend on them.

If we wish to develop autonomous vehicles that are able to navigate directly toward a goal at high speeds through unknown environments, then we must move beyond the simple range-sensor based techniques. We must develop algorithms that enable autonomous agents to harness knowledge about the structure of the world to interpret noisy and ambiguous sensor information, and make inferences about about parts of the world that cannot be directly observed.

In this thesis we develop a new representation based around a library of trajectory bundles, that makes this challenging task more tractable. Rather than attempt to explicitly model the geometry of the world in front of the vehicle (which can be incredibly complex), we reason about the world in terms of abstract notions about what the vehicle can and cannot do. Trajectory bundles provide a lens through which we can look at perception tasks for navigation, allowing us to leverage machine learning tools in much more effective ways. We develop a Bayesian filtering framework that enables us to estimate a belief over which trajectory bundles are feasible based on the history of actions and observations of the vehicle, resulting in improved navigation performance.

See other events happening in December 2012