LiDAR-Based Object Tracking and Shape Estimation Using Polylines and Free-Space Information

Kraemer, Stefan; Stiller, Christoph; Bouzouraa, Mohamed Essayed

doi:10.1109/iros.2018.8593385

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…In [ 32 , 33 ], the authors underline that the cuboid representation is not suitable for objects because it overestimates the space occupied by non-L-shaped objects, such as a circular fence or a more complex building. A better representation of the objects is by polylines or facets.…”

Section: Related Workmentioning

confidence: 99%

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Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Dulău

Oniga

2021

Sensors

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In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.

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Section: Related Workmentioning

confidence: 99%

“…In [ 33 ], objects are represented as polylines, a polyline segment being the base structure of a facet. Their quantitative evaluation is based on the orientation angle of the object and the results show that representation using polyline is closer to the ground truth than the cuboid representation.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Dulău

Oniga

2021

Sensors

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“…Based on the point cloud data, it is also possible to estimate the moving obstacles' position and velocity in the self-driving cars [14]- [15]. However, they all rely on high-quality point clouds from LiDAR sensors and powerful GPUs to detect obstacles from only predefined classes.…”

Section: Related Workmentioning

confidence: 99%

Real-time Identification and Simultaneous Avoidance of Static and Dynamic Obstacles on Point Cloud for UAVs Navigation

Chen¹,

Lu²

2021

Preprint

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Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a more robust technique to distinguish and track dynamic obstacles from static ones with only point cloud input. Then, to achieve dynamic avoidance, we propose the forbidden pyramids method to solve the desired vehicle velocity with an efficient sampling-based method in iteration. The motion primitives are generated by solving a nonlinear optimization problem with the constraint of desired velocity and the waypoint. Furthermore, we present several techniques to deal with the position estimation error for close objects, the error for deformable objects, and the time gap between different submodules. The proposed approach is implemented to run onboard in real-time and validated extensively in simulation and hardware tests, demonstrating our superiority in tracking robustness, energy cost, and calculating time.

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“…To classify and recognize objects (like pedestrians, trees, or vehicles), lidars make use of techniques such as machine learning based on object recognition [ 106 , 107 , 108 , 109 ], and additional methods such as global and local extraction of features to help in providing the structure of the target. Lidar uses the Bayesian filtering framework and data association methods for target tracking and motion prediction to provide information, such as velocity, trajectory, and object positioning [ 110 , 111 , 112 ]. In contrast to radar-based multi-object tracking, in which all detections are typically represented as points, lidar-based multi-tracking provides detection patterns of targets and this property of lidar scanning causes users to opt for lidar.…”

Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

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LiDAR-Based Object Tracking and Shape Estimation Using Polylines and Free-Space Information

Cited by 18 publications

References 15 publications

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Real-time Identification and Simultaneous Avoidance of Static and Dynamic Obstacles on Point Cloud for UAVs Navigation

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

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