6 accepted papers
Recent advances in metric, semantic, and topological mapping have equipped autonomous robots with concept grounding capabilities to interpret natural language tasks. Leveraging these capabilities, this work develops an efficient task planning algorithm for hierarchical metric-semantic models. We con…
In this paper, we develop an approach that enables autonomous robots to build and compress semantic environment representations from point-cloud data. Our approach builds a three-dimensional, semantic tree representation of the environment from raw sensor data which is then compressed by a novel inf…
This paper proposes a method for learning continuous control policies for exploration and active landmark localization. We consider a mobile robot detecting landmarks within a limited sensing range, and tackle the problem of learning a control policy that maximizes the mutual information between the…
The problem of active mapping aims to plan an informative sequence of sensing views given a limited budget such as distance traveled. This paper considers active occupancy grid mapping using a range sensor, such as LiDAR or depth camera. State-of-the-art methods optimize information-theoretic measur…
Many robot applications call for autonomous exploration and mapping of unknown and unstructured environments. Information-based exploration techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and fast Shannon mutual information (FSMI), have successfully achieved active binary occ…
This paper develops iterative Covariance Regulation (iCR), a novel method for active exploration and mapping for a mobile robot equipped with on-board sensors. The problem is posed as optimal control over the SE(3) pose kinematics of the robot to minimize the differential entropy of the map conditio…