Entries by Lily Hoot

Hierarchical Planning for Heterogeneous Multi-Robot Routing Problems via Learned Subteam Performance

A recent paper by members of the DCIST alliance proposes a new hierarchical planner for task allocation problems where tasks correspond to heterogeneous multi-robot routing problems defined on different areas of a given environment. The researchers tackled this complex planning problem with a novel planner which breaks down the complexity of the original problem into […]

CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration

This paper considers online object-level mapping using partial point-cloud observations obtained online in an unknown environment. We develop an approach for fully Convolutional Object Retrieval and Symmetry-AIded Registration (CORSAIR). Our model extends the Fully Convolutional Geometric Features (FCGF) model to learn a global object-shape embedding in addition to local point-wise features from the point-cloud observations. […]

Robust multimodal data association

A fundamental problem in robotic perception is matching identical objects or data, with applications such as loop closure detection, place recognition, object tracking, and map fusion. The problem becomes more challenging when matching is done jointly across multiple, multimodal sets of data, however, the robustness and accuracy of matching in the presence of noise and […]

ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description

A recent paper by members of the DCIST alliance develops a method for tightly coupled object shape and pose optimization. Inspired by DeepSDF, which uses neural networks to regress a Signed Distance Function (SDF) description of object shape, they propose a bi-level object shape model named ELLIPSDF, to support joint object pose and shape optimization. […]

Robust data association in high-outlier regimes

Establishing correspondence between two sets of data is a fundamental problem in robotics, and is required for fusing data across multiple DCIST agents to establish global situational awareness. Real-world data contains noise and outliers. The traditional linear assignment algorithms are not robust to high-outlier regimes, leading to incorrect correspondences. To address these issues,  members of […]

Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation

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 occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful objects, it is […]

Multi-robot Scheduling for Environmental Monitoring as a Team Orienteering Problem

We develop an evolutionary algorithm for solving the multi-robot orienteering problem where a team of cooperative robots aims to maximize the total information collected by visiting a subset of given nodes within a fixed budget on travel costs. Multi-robot orienteering problems are relevant to applications such as logistic delivery services, precision agriculture, and environmental sampling […]

Optimizing Non-Markovian Information Gain Under Physics-Based Communication Constraints

A recent paper by members of DCIST proposes an exploration method that maintains communication between all robot team members and a static base station. By maintaining communication while exploring, robots are kept up to date on the progress of other team members and important information—e.g., survivors in a search and rescue mission—are quickly transmitted to […]

Active Exploration and Mapping via Iterative Covariance Regulation over Continuous SE(3) Trajectories

A recent paper by the members of the DCIST alliance develops a method for continuous-space optimal control of active information acquisition. They have developed “iterative Covariance Regulation (iCR)”, a novel method for an information-theoretic active perception performing multi-step forward-backward gradient descent. The problem is formalized as SE(3) trajectory optimization over a multi-step continuous control sequence […]