My research focus is on controlling teams of robots to collect information about physical processes of interest in applications such as environmental monitoring, security and surveillance, localization and mapping, construction and structure inspection, agriculture, and search and rescue. The message of my work is that transitioning from static, manual data collection to autonomous robot deployments will result in accurate and efficient sampling, higher-fidelity models, reduced human supervision, access to dangerous areas, and adaptation to environment conditions. My research has contributed towards this vision by focusing on control and estimation techniques for multi-robot active information acquisition and on semantic representations for localization, mapping, and navigation.
Video 1: Decentralized multi-robot active SLAM
Video 2: Wireless radio source seeking
Video 3: Active object recognition with a mobile depth camera
Video 4: Active deformable part models for faster object detection
A complementary direction to developing scalable information-seeking techniques is devising better representations for localization, mapping, and navigation. The recent impressive progress in robotics is due in large part to the advances in inference techniques based on graphical models and simultaneous localization and mapping (SLAM) algorithms. For example, flying a quadrotor away from the motion capture system would not be possible without SLAM, just like Google's Project Tango would be nothing more than an Andorid phone. While existing SLAM approaches are quite mature, they rely heavily on (continuous Gaussian) geometric features such as points, lines, and planes and cannot handle (discrete) semantic information such as recognized objects in the robot's surroundings.
Our contribution is a sensor measurement model for set-valued observations that captures both metric and semantic information and incorporates missed and false detections and unknown data association. We proved that obtaining the likelihood of a set-valued observation is equivalent to a matrix permanent computation. This crucial transformation led to an efficient polynomial-time approximation of Bayesian filtering with set-valued observations. These ideas enable robot and vehicle localization in residential areas using semantic information from object recognition (Video 5, Video 7) and global localization of Google's Project Tango phone. We have extended these results to semantic mapping (Video 6) and active localization, in which the vehicle trajectory is optimized (using ideas from active information gathering) in order to improve the localization performance. Finally, one can go beyond SLAM and formulate high-level missions for the robot described in terms of the objects on the map. We developed an approach for motion planning under linear temporal logic (LTL) constraints in probabilistic semantic maps. We proved that the stochastic LTL planning problem can be reduced to a deterministic shortest path problem while maintaining probabilistic correctness guarantees. This allows computing the optimal robot trajectory in the subspace of deterministic plans that guarantee a given probability of satisfying the logic specification.Relevant publications: [RSS'14][IJRR'15][ICRA'16]
Video 5: Vehicle localization in a residential area using object recognition
Video 6: Simultaneous localization and mapping using semantic information
Video 7: Mobile robot localization using object recognition