Paper Info
Title
Particle Filter Refinement Based On Clustering Procedures For High-Dimensional Localization And Mapping Systems
Abstract
Developing safe autonomous robotic applications for outdoor agricultural environments is a research field that still presents many challenges. Simultaneous Localization and Mapping can be crucial to endow the robot to localize itself with accuracy and, consequently, perform tasks such as crop monitoring and harvesting autonomously. In these environments, the robotic localization and mapping systems usually benefit from the high density of visual features. When using filter-based solutions to localize the robot, such an environment usually uses a high number of particles to perform accurately. These two facts can lead to computationally expensive localization algorithms that are intended to perform in real-time. This work proposes a refinement step to a standard high-dimensional filter based localization solution through the novelty of downsampling the filter using an online clustering algorithm and applying a scan-match procedure to each cluster. Thus, this approach allows scan matchers without high computational cost, even in high dimensional filters. Experiments using real data in an agricultural environment show that this approach improves the Particle Filter performance estimating the robot pose. Additionally, results show that this approach can build a precise 3D reconstruction of agricultural environments using visual scans, i.e., 3D scans with RGB information. (c) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.robot.2021.103725
ROBOTICS AND AUTONOMOUS SYSTEMS
Keywords
DocType
Volume
SLAM, Clustering, Agricultural robotics
Journal
137
ISSN
Citations 
PageRank 
0921-8890
1
0.36
References 
Authors
0
5
Name
Order
Citations
PageRank
André Silva Aguiar140.79
Filipe Neves dos Santos22312.24
Héber Sobreira340.79
José Boaventura Cunha410.36
Armando Jorge Sousa540.79