Work

Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation

Year: 2024

Type: conference-paper

Abstract: We address the problem of sparse selection of visual features for localizing a team of robots navigating in an unknown environment, where robots can exchange relative position measurements with neighbors. We select a set of the most informative features by anticipating their importance in robots localization by simulating trajectories of robots over a prediction horizon. Through theoretical proofs, we establish a crucial connection between graph Laplacian and the importance of features. We leverage a scalable randomized algorithm for sparse sums of positive semidefinite matrices to efficiently select a set of the most informative features.

Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI: 10.1109/iros58592.2024.10802446

Authors: Vivek Pandey, Arash Amini, Guangyi Liu, Ufuk Topcu, Qiyu Sun, Kostas Daniilidis, Nader Motee

Institutions: Lehigh University, The University of Texas at Austin, University of Central Florida, University of Pennsylvania

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4405787307

FWCI: 1.664

Cites: 15

Cited by: 2

Related to: 10