Multidimensional Particle Filter for Long-Term Visual Teach and Repeat in Changing Environments

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00364991" target="_blank" >RIV/68407700:21230/23:00364991 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/LRA.2023.3244418" target="_blank" >https://doi.org/10.1109/LRA.2023.3244418</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/LRA.2023.3244418" target="_blank" >10.1109/LRA.2023.3244418</a>

Result language

angličtina

Original language name

Multidimensional Particle Filter for Long-Term Visual Teach and Repeat in Changing Environments

Original language description

When a mobile robot is asked to navigate intelligently in an environment, it needs to estimate its own and the environment's state. One of the popular methods for robot state and position estimation is particle filtering (PF). Visual Teach and Repeat (VT & R) is a type of navigation that uses a camera to navigate the robot along the previously traversed path. Particle filters are usually used in VT & R to fuse data from odometry and camera to estimate the distance traveled along the path. However, in VT & R, there are other valuable states that the robot can benefit from, especially when moving through changing environments. We propose a multidimensional particle filter to estimate the robot state in VT & R navigation. Apart from the traveled distance, our particle filter estimates lateral and heading deviation from the taught path as well as the current appearance of the environment. This appearance is estimated using maps created across various environmental conditions recorded during the previous traversals. The joint state estimation is based on contrastive neural network architecture, allowing self-supervised learning. This architecture can process multiple images in parallel, alleviating the potential overhead caused by computing the particle filter over the maps simultaneously. We conducted experiments to show that the joint robot/environment state estimation improves navigation accuracy and robustness in a continual mapping setup. Unlike the other frameworks, which treat the robot position and environment appearance separately, our PF represents them as one multidimensional state, resulting in a more general uncertainty model for VT & R.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Robotics and Automation Letters

ISSN

2377-3766

e-ISSN

2377-3766

Volume of the periodical

8

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

8

Pages from-to

1951-1958

UT code for WoS article

000937134700010

EID of the result in the Scopus database

2-s2.0-85149121338