SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm

The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM...

Full description

Saved in:
Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 19; no. 17; p. 3699
Main Authors Bahraini, Masoud S., Rad, Ahmad B., Bozorg, Mohammad
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 26.08.2019
MDPI
Subjects
Algorithms
autonomous robot
Classification
DATMO
Deep learning
Lasers
Localization
Mechanical engineering
Methods
multi-target tracking
Neural networks
Product development
R-CNN
RANSAC
Robots
Semantics
Sensors
SLAM
Iran
United States > US
RANSAC
R-CNN
deep learning
SLAM
DATMO
multi-target tracking
autonomous robot
Online AccessGet full text
ISSN1424-8220
1424-8220
DOI10.3390/s19173699

Cover

More Information
Summary:The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s19173699