SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm
| Autor: | Ahmad B. Rad, Masoud S. Bahraini, Mohammad Bozorg |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Data stream
QA75 0209 industrial biotechnology Computer science TK 02 engineering and technology RANSAC Simultaneous localization and mapping lcsh:Chemical technology Biochemistry Article autonomous robot Analytical Chemistry Extended Kalman filter 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering R-CNN Computer vision lcsh:TP1-1185 Electrical and Electronic Engineering QA Instrumentation business.industry Deep learning deep learning Autonomous robot Atomic and Molecular Physics and Optics Lidar multi-target tracking Feature (computer vision) Video tracking SLAM 020201 artificial intelligence & image processing Artificial intelligence DATMO business |
| Zdroj: | Sensors, Vol 19, Iss 17, p 3699 (2019) Sensors Sensors (Basel, Switzerland) Volume 19 Issue 17 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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