2D laser-based probabilistic motion tracking in urban-like environments

Becker, Marcelo; Hall, Richard; Kolski, Sascha; Maček, Kristijan; Siegwart, Roland; Jensen, B.

doi:10.1590/s1678-58782009000200001

Cited by 9 publications

(5 citation statements)

References 43 publications

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“…Perera et al [14] analyzed and tracked multiple ship conditions, combined ship trajectory detection with shipping state estimation, and simulated ship trajectories. Berker et al [15] applied a two-dimensional (2D) obstacle motion-tracking module to a dynamometer tracking algorithm to improve data quality for navigation purposes. Stubberud and Kramer [16] used a neural-extension Kalman filter to dynamically predict a target state online, thus improving the state estimation capability of existing models.…”

Section: Introductionmentioning

confidence: 99%

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Gao

Shi

2018

Sensors

101

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The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection systems.

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Section: Introductionmentioning

confidence: 99%

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Gao

Shi

2018

Sensors

101

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“…Recently, the visual SLAM is actively studied, however it is hard to implement in real-time because of its large computational load. In this reason, scan matching method using a laser scanner is popular because a laser scanner has fast in data acquisition, fine resolution and high accuracy [4][5][6]. Many of previous researches have assumptions about dynamic model, and tracking the dynamic obstacle using corrected robot position after scan matching.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Obstacle Detection Using Visually Informed Scan Matching

Lee

Yoo

et al. 2013

AMM

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This paper presents a real-time dynamic obstacle detection algorithm using a scan matching method considering image information from a mobile robot equipped with a camera and a laser scanner. By combining image and laser scan data, we extract a scan segment corresponding to the dynamic obstacle. To complement the performance of scan matching, poor in dynamic environments, the extracted scan segment is temporarily removed. After obtaining a good robot position, the position of the dynamic obstacle is calculated based on the robot’s position. Through two experimental scenarios, the performance of the proposed algorithm is demonstrated.

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“…The use of the fusion of LiDAR and camera look promising, not only for detection but also for classification. Becker et al (BECKER et al , 2007) (BECKER et al , 2009 used LiDAR in front of the vehicle to detect and classify obstacles on the road, and for such sensors, developed an algorithm for detecting the movement of those objects. To initially sort the objects, they grouped the detected points based on the fact that near points belong to the same object.…”

Section: Review Of Related Workmentioning

confidence: 99%

Object detection and classication in outdoor environments for autonomous passenger vehicle navigation based on Data Fusion of Articial Vision System and LiDAR sensor

Velandia¹

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2D laser-based probabilistic motion tracking in urban-like environments

Cited by 9 publications

References 43 publications

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Dynamic Obstacle Detection Using Visually Informed Scan Matching

Object detection and classication in outdoor environments for autonomous passenger vehicle navigation based on Data Fusion of Articial Vision System and LiDAR sensor

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