ArticleSensors (Basel, Switzerland)2019
SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Micro 4D Imaging Sensor Using Snapshot Narrowband Imaging Method.Micromachines · 2023Article
- CNN-Based Fault Detection of Scan Matching for Accurate SLAM in Dynamic Environments.Sensors (Basel, Switzerland) · 2023Article
- RANSAC for Robotic Applications: A Survey.Sensors (Basel, Switzerland) · 2022Review
- A New Coarse Gating Strategy Driven Multidimensional Assignment for Two-Stage MHT of Bearings-Only Multisensor-Multitarget Tracking.Sensors (Basel, Switzerland) · 2022Article
- Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence.Computational intelligence and neuroscience · 2022Article
- Evaluation of the Intel RealSense T265 for tracking natural human head motion.Scientific reports · 2021Observational
- Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System.Sensors (Basel, Switzerland) · 2020Article
- Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
3 authors.
Funding
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Abstract
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.
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Registered trials
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