State transition for statistical SLAM using planar features in 3D point clouds

Khodadadian Gostar, A, Fu, C, Chuah, W, Hossain, M, Tennakoon, R, Bab-Hadiashar, A and Hoseinnezhad, R 2019, 'State transition for statistical SLAM using planar features in 3D point clouds', Sensors, vol. 19, no. 7, pp. 1-17.

Document type: Journal Article
Collection: Journal Articles

Title State transition for statistical SLAM using planar features in 3D point clouds
Author(s) Khodadadian Gostar, A
Fu, C
Chuah, W
Hossain, M
Tennakoon, R
Bab-Hadiashar, A
Hoseinnezhad, R
Year 2019
Journal name Sensors
Volume number 19
Issue number 7
Start page 1
End page 17
Total pages 17
Publisher MPDIAG
Abstract There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications.
Subject Computer Vision
Keyword(s) Bayesian filters
Planar features
Plane parameters
Simultaneous localization and mapping
Transition model
DOI - identifier 10.3390/s19071614
Copyright notice © 2019 by the authors. Licensee MDPI, Basel, Switzerland. Creative Commons Attribution (CC BY) license (
ISSN 1424-8220
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