Object detection and tracking using multi-layer laser for autonomous urban driving

Ye, Yutong; Fu, Liming; Li, Bijun

doi:10.1109/itsc.2016.7795564

Cited by 40 publications

(38 citation statements)

References 4 publications

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“…The pose estimation using model-based methods offer optimal results, however, at a cost of high computational burdens. On the other hand, feature-based methods rely on edge features to deduce the pose of an object [9,11,19,37]. The feature-based estimations are computationally efficient; however, these are sensitive to unstable measurements.…”

Section: Object Detectionmentioning

confidence: 99%

“…Other works have also considered learning based neural network models to estimate the object pose [38], by training the detectors from all possible view angles. In this work, minimum rectangle area [39] with L-shape cloud fitting [9] is utilized as in [13,40], with optimized computational and accuracy considerations.…”

Section: Object Detectionmentioning

confidence: 99%

“…Another approach presented in [7], used JPDAF to track a limited number of dynamic objects detected by comparing consecutive LiDAR point clouds. Similarly, [8,9] exploit single LiDAR measurement patterns for object classification that bars the use of multiple LiDARs and require additional computational resources. The methods proposed in [8,10] are intended for stationary reference frames, whereas the work presented in [11] only considers tracking pedestrians.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Sualeh

Kim

2019

Sensors

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Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception. The merged LiDAR data is treated with an efficient MODT framework, considering the limitations of the vehicle-embedded computing environment. The ground classification is obtained through a grid-based method while considering a non-planar ground. Furthermore, unlike prior works, 3D grid-based clustering technique is developed to detect objects under elevated structures. The centroid measurements obtained from the object detection are tracked using Interactive Multiple Model-Unscented Kalman Filter-Joint Probabilistic Data Association Filter (IMM-UKF-JPDAF). IMM captures different motion patterns, UKF handles the nonlinearities of motion models, and JPDAF associates the measurements in the presence of clutter. The proposed algorithm is implemented on two slightly dissimilar platforms, giving real-time performance on embedded computers. The performance evaluation metrics by MOT16 and ground truths provided by KITTI Datasets are used for evaluations and comparison with the state-of-the-art. The experimentation on platforms and comparisons with state-of-the-art techniques suggest that the proposed framework is a feasible solution for MODT tasks.

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Section: Object Detectionmentioning

confidence: 99%

Section: Object Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Sualeh

Kim

2019

Sensors

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“…In our method, a fast object segmentation and tracking algorithm was required. In previous studies [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], the authors proposed numerous detection and tracking-of-moving-objects (DATMO) systems. However, DATMO systems are time-consuming because of the high computation complexity.…”

Section: Related Workmentioning

confidence: 99%

Multimedia System for Real-Time Photorealistic Nonground Modeling of 3D Dynamic Environment for Remote Control System

et al. 2018

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Nowadays, unmanned ground vehicles (UGVs) are widely used for many applications. UGVs have sensors including multi-channel laser sensors, two-dimensional (2D) cameras, Global Positioning System receivers, and inertial measurement units (GPS-IMU). Multi-channel laser sensors and 2D cameras are installed to collect information regarding the environment surrounding the vehicle. Moreover, the GPS-IMU system is used to determine the position, acceleration, and velocity of the vehicle. This paper proposes a fast and effective method for modeling nonground scenes using multiple types of sensor data captured through a remote-controlled robot. The multi-channel laser sensor returns a point cloud in each frame. We separated the point clouds into ground and nonground areas before modeling the three-dimensional (3D) scenes. The ground part was used to create a dynamic triangular mesh based on the height map and vehicle position. The modeling of nonground parts in dynamic environments including moving objects is more challenging than modeling of ground parts. In the first step, we applied our object segmentation algorithm to divide nonground points into separate objects. Next, an object tracking algorithm was implemented to detect dynamic objects. Subsequently, nonground objects other than large dynamic ones, such as cars, were separated into two groups: surface objects and non-surface objects. We employed colored particles to model the non-surface objects. To model the surface and large dynamic objects, we used two dynamic projection panels to generate 3D meshes. In addition, we applied two processes to optimize the modeling result. First, we removed any trace of the moving objects, and collected the points on the dynamic objects in previous frames. Next, these points were merged with the nonground points in the current frame. We also applied slide window and near point projection techniques to fill the holes in the meshes. Finally, we applied texture mapping using 2D images captured using three cameras installed in the front of the robot. The results of the experiments prove that our nonground modeling method can be used to model photorealistic and real-time 3D scenes around a remote-controlled robot.

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“…Light detection and ranging (LIDAR) is a substantial contributing sensor for autonomous driving [ 1 , 2 ], 3D reconstruction [ 3 , 4 , 5 ], simultaneous localization and mapping (SLAM) [ 6 , 7 , 8 , 9 , 10 ] and visual navigation [ 11 , 12 , 13 , 14 ], etc. With the benefits of high accuracy and ignoring of background illumination, the rotating two-dimensional light detection and ranging (R2D-LIDAR) becomes an attractive sensor for outdoor mobile robots [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Calibration Method for a Rotating 2D LIDAR System

Zeng

Dai

et al. 2018

Sensors

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This paper presents an improved calibration method of a rotating two-dimensional light detection and ranging (R2D-LIDAR) system, which can obtain the 3D scanning map of the surroundings. The proposed R2D-LIDAR system, composed of a 2D LIDAR and a rotating unit, is pervasively used in the field of robotics owing to its low cost and dense scanning data. Nevertheless, the R2D-LIDAR system must be calibrated before building the geometric model because there are assembled deviation and abrasion between the 2D LIDAR and the rotating unit. Hence, the calibration procedures should contain both the adjustment between the two devices and the bias of 2D LIDAR itself. The main purpose of this work is to resolve the 2D LIDAR bias issue with a flat plane based on the Levenberg–Marquardt (LM) algorithm. Experimental results for the calibration of the R2D-LIDAR system prove the reliability of this strategy to accurately estimate sensor offsets with the error range from −15 mm to 15 mm for the performance of capturing scans.

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Object detection and tracking using multi-layer laser for autonomous urban driving

Cited by 40 publications

References 4 publications

Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Multimedia System for Real-Time Photorealistic Nonground Modeling of 3D Dynamic Environment for Remote Control System

An Improved Calibration Method for a Rotating 2D LIDAR System

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