Moving object detection with laser scanners

Mertz, Christoph; Navarro-Serment, Luis E.; MacLachlan, Robert A.; Rybski, Paul E.; Steinfeld, Aaron; Suppé, Arne; Urmson, Chris; Vandapel, Nicolas; Hebert, Martial; Thorpe, C.; Duggins, David; Gowdy, Jay

doi:10.1002/rob.21430

Cited by 127 publications

(86 citation statements)

References 54 publications

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“…Classic approaches for object detection in lidar point clouds use clustering algorithms to segment the data, assigning the resulting groups to different classes [2,27,6,18]. Other strategies, such as the one used as baseline method in this paper, benefit from prior knowledge of the environment structure to ease the object segmentation and clustering [20,24].…”

Section: Related Workmentioning

confidence: 99%

Low Resolution Lidar-Based Multi-Object Tracking for Driving Applications

Pino

Vaquero

Masini

et al. 2017

ROBOT 2017: Third Iberian Robotics Conference

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Abstract. Vehicle detection and tracking in real scenarios are key components to develop assisted and autonomous driving systems. Lidar sensors are specially suitable for this task, as they bring robustness to harsh weather conditions while providing accurate spatial information. However, the resolution provided by point cloud data is very scarce in comparison to camera images. In this work we explore the possibilities of Deep Learning (DL) methodologies applied to low resolution 3D lidar sensors such as the Velodyne VLP-16 (PUCK), in the context of vehicle detection and tracking. For this purpose we developed a lidar-based system that uses a Convolutional Neural Network (CNN), to perform pointwise vehicle detection using PUCK data, and Multi-Hypothesis Extended Kalman Filters (MH-EKF), to estimate the actual position and velocities of the detected vehicles. Comparative studies between the proposed lower resolution (VLP-16) tracking system and a high-end system, using Velodyne HDL-64, were carried out on the Kitti Tracking Benchmark dataset. Moreover, to analyze the influence of the CNN-based vehicle detection approach, comparisons were also performed with respect to the geometric-only detector. The results demonstrate that the proposed low resolution Deep Learning architecture is able to successfully accomplish the vehicle detection task, outperforming the geometric baseline approach. Moreover, it has been observed that our system achieves a similar tracking performance to the high-end HDL-64 sensor at close range. On the other hand, at long range, detection is limited to half the distance of the higher-end sensor.

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Section: Related Workmentioning

confidence: 99%

Low Resolution Lidar-Based Multi-Object Tracking for Driving Applications

Pino

Vaquero

Masini

et al. 2017

ROBOT 2017: Third Iberian Robotics Conference

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“…Typical algorithms takes the general approach (extended from Computer Vision algorithms) of dividing between object detection in static frames (fixed time) and tracking the detected objects along the time [9], [8].…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Low Cost, Robust and Real Time System for Detecting and Tracking Moving Objects to Automate Cargo Handling in Port Terminals

Vaquero

Repiso

Sanfeliu

et al. 2015

Advances in Intelligent Systems and Computing

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Abstract. The presented paper addresses the problem of detecting and tracking moving objects for autonomous cargo handling in port terminals using a perception system which input data is a single layer laser scanner. A computationally low cost and robust Detection and Tracking Moving Objects (DATMO) algorithm is presented to be used in autonomous guided vehicles and autonomous trucks for efficient transportation of cargo in ports. The method first detects moving objects and then tracks them, taking into account that in port terminals the structure of the environment is formed by containers and that the moving objects can be trucks, AGV, cars, straddle carriers and people among others. Two approaches of the DATMO system have been tested, the first one is oriented to detect moving obstacles and focused on tracking and filtering those detections; and the second one is focused on keepking targets when no detections are provided. The system has been evaluated with real data obtained in the CTT port terminal in Hengelo, the Netherlands. Both methods have been tested in the dataset with good results in tracking moving objects.

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“…This drawback can be addressed by using higher power lasers and optical filters at the wavelength of the utilized laser. The laser ranging system typical covers an area of a half-circle with a 50-m radius for large objects (more than 1 m as viewed from the scanner) (Mertz, 2013). The CMU-RI Navlab group has developed such a system that uses a laser scanner as its primary sensor.…”

Section: Introduction 1234mentioning

confidence: 99%

“…The CMU-RI Navlab group has developed such a system that uses a laser scanner as its primary sensor. (Mertz, 2013). The MIT Urban Grand Challenge team has made their data available (Huang, 2010).…”

Section: Introduction 1234mentioning

confidence: 99%

A Novel Low-Cost Adaptive Scanner Concept for Mobile Robots

Stančić¹,

Musić²,

Cecić³

2014

ing.inv.

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A fundamental problem in mobile robot applications is the need for accurate knowledge of the position of a vehicle for localizing itself and for avoiding obstacles in its path. In the search for a solution to this problem, researchers and engineers have developed different sensors, systems and techniques. Modern mobile robots relay information obtained from a variety of sensors and sophisticated data fusion algorithms. In this paper, a novel concept for a low-cost adaptive scanner based on a projected light pattern is proposed. The main advantage of the proposed system is its adaptivity, which enables the rapid scanning of the robot's surroundings in search of obstacles and a more detailed scan of a single object to retrieve its surface configuration and perform some limited analyses. This paper addresses the concept behind such a scanner, where a proof-of-concept is achieved using an office DLP projector. During the measurements, the accuracy of the proposed system was tested on obstacles and objects with known configurations. The obtained results are presented and analyzed, and conclusions about the system's performance and possible improvements are discussed.Keywords: Range finder, 3D scanner, mobile robot, structured light, laser, projector. RESUMENEl problema fundamental en las aplicaciones de robots móviles, es la necesidad de conocer con exactitud la posición del vehículo, para poder localizarse en el espacio y evitar obstáculos en su camino. En la búsqueda de una solución, los investigadores e ingenieros han desarrollado diferentes sensores, sistemas y técnicas.Los robots móviles modernos se basan en la información obtenida de diferentes sensores y en sofisticados algoritmos de fusión de datos. Por tal razón, en este artículo se propone un nuevo concepto de escáner de adaptación a bajo costo, basándose en patrones de luz proyectados. La ventaja principal del sistema propuesto es: su adaptabilidad, que permite en los robots el escaneo rápido de los alrededores durante la búsqueda de obstáculos y una exploración más detallada de un objeto determinado, para poder recuperar así, su configuración de la superficie y realizar algunos análisis limitados. El artículo aborda el concepto de un escáner de este tipo, donde se logró la prueba del concepto utilizando un proyector de oficina DLP. Durante las mediciones, la exactitud del sistema propuesto se puso a prueba, usando obstáculos con objetos de configuraciones conocidas. De esta manera, los resultados obtenidos son presentados, analizados y se discuten las conclusiones sobre el desempeño del sistema para generar posibles mejoramientos.Palabras clave: telémetro, escáner 3D, robot móvil, luz estructurada, laser y proyector.

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Moving object detection with laser scanners

Cited by 127 publications

References 54 publications

Low Resolution Lidar-Based Multi-Object Tracking for Driving Applications

Low Resolution Lidar-Based Multi-Object Tracking for Driving Applications

Low Cost, Robust and Real Time System for Detecting and Tracking Moving Objects to Automate Cargo Handling in Port Terminals

A Novel Low-Cost Adaptive Scanner Concept for Mobile Robots

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