Visual Teach and Generalise (VTAG)-Exploiting Perceptual Aliasing for Scalable Autonomous Robotic Navigation in Horticultural Environments

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00367994" target="_blank" >RIV/68407700:21230/23:00367994 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.compag.2023.108054" target="_blank" >https://doi.org/10.1016/j.compag.2023.108054</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compag.2023.108054" target="_blank" >10.1016/j.compag.2023.108054</a>

Result language

angličtina

Original language name

Visual Teach and Generalise (VTAG)-Exploiting Perceptual Aliasing for Scalable Autonomous Robotic Navigation in Horticultural Environments

Original language description

Nowadays, most agricultural robots rely on precise and expensive localisation, typically based on global navigation satellite systems (GNSS) and real-time kinematic (RTK) receivers. Unfortunately, the precision of GNSS localisation significantly decreases in environments where the signal paths between the receiver and the satellites are obstructed. This precision hampers deployments of these robots in, e.g., polytunnels or forests. An attractive alternative to GNSS is vision-based localisation and navigation. However, perceptual aliasing and landmark deficiency, typical for agricultural environments, cause traditional image processing techniques, such as feature matching, to fail. We propose an approach for an affordable pure vision-based navigation system which is not only robust to perceptual aliasing, but it actually exploits the repetitiveness of agricultural environments. Our system extends the classic concept of visual teach and repeat to visual teach and generalise (VTAG). Our teach and generalise method uses a deep learning-based image registration pipeline to register similar images through meaningful generalised representations obtained from different but similar areas. The proposed system uses only a low-cost uncalibrated monocular camera and the robot's wheel odometry to produce heading corrections to traverse crop rows in polytunnels safely. We evaluate this method at our test farm and at a commercial farm on three different robotic platforms where an operator teaches only a single crop row. With all platforms, the method successfully navigates the majority of rows with most interventions required at the end of the rows, where the camera no longer has a view of any repeating landmarks such as poles, crop row tables or rows which have visually different features to that of the taught row. For one robot which was taught one row 25 m long our approach autonomously navigated the robot a total distance of over 3.5 km, reaching a teach-generalisation gain of 140.

Czech name

—

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

Computers and Electronics in Agriculture

ISSN

0168-1699

e-ISSN

1872-7107

Volume of the periodical

212

Issue of the periodical within the volume

September

Country of publishing house

CH - SWITZERLAND

Number of pages

11

Pages from-to

1-11

UT code for WoS article

001051915100001

EID of the result in the Scopus database

2-s2.0-85166487705