Joint Feature Selection and Time Optimal Path Parametrization for High Speed Vision-Aided Navigation
Name
Spasojevic-IROS20.pdf
Description
Accepted version
Size
2.2 MB
Format
Adobe PDF
Checksum (MD5)
6881b05d13da74f604a4f35e5d258d81
Author(s)
Spasojevic, Igor
Murali, Varun
Karaman, Sertac
Date Issued
February 2021
Journal
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Spasojevic, Igor et al. "Joint Feature Selection and Time Optimal Path Parametrization for High Speed Vision-Aided Navigation." 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2020-January 2021, Las Vegas, Nevada (virtual event), Institute of Electrical and Electronics Engineers, February 2021. © 2020 IEEE
Version
Author's final manuscript
Abstract
We study a problem in vision-aided navigation in which an autonomous agent has to traverse a specified path in minimal time while ensuring extraction of a steady stream of visual percepts with low latency. Vision-aided robots extract motion estimates from the sequence of images of their on-board cameras by registering the change in bearing to landmarks in their environment. The computational burden of the latter procedure grows with the range of apparent motion undertaken by the projections of the landmarks, incurring a lag in pose estimates that should be minimized while navigating at high speeds. This paper addresses the problem of selecting a desired number of landmarks in the environment, together with the time parametrization of the path, to allow the agent execute it in minimal time while both (i) ensuring the computational burden of extracting motion estimates stays below a set threshold and (ii) respecting the actuation constraints of the agent. We provide two efficient approximation algorithms for addressing the aforementioned problem. Also, we show how it can be reduced to a mixed integer linear program for which there exist well-developed optimization packages. Ultimately, we illustrate the performance of our algorithms in experiments using a quadrotor.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros45743.2020.9341654
