Multimodal sparse LIDAR object tracking in clutter

Muresan, Mircea Paul; Nedevschi, Sergiu

doi:10.1109/iccp.2018.8516646

Cited by 12 publications

(12 citation statements)

References 24 publications

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“…The main characteristics of the GPS system that was used to obtain the information from a target vehicle are displayed in Table 1 [48]. We refer to the vehicle on which the GPS is mounted as the target vehicle and the car on which the sensors are mounted as the reference (or ego) vehicle.…”

Section: Methodsmentioning

confidence: 99%

Section: Data Association and Tracking Of The 16 L Lidar Objectsmentioning

confidence: 97%

“…Before performing the sensor fusion task, the 3D objects, which come from an earlier module in the pipeline, should have a filtered position, velocity and unique ID assigned to each of them. A two-step tracking algorithm based on [48] is used. The 3D objects are transformed into 2D virtual objects, embedding all properties of the original 3D cuboids: the original x, y, z position, the width and length of the original object, the x, y positions in the 2D grid, the width and length of the object in the 2D grid, the top three semantic classes, the marginal probabilities of each of the classes, and an eight-bin color histogram for each of the three channels (R, G, B).…”

Section: Data Association and Tracking Of The 16 L Lidar Objectsmentioning

confidence: 99%

“…The proposed two-step data association and tracking procedure based on the two motion model filters, which can be seen in Figure 13, is an improvement of the algorithm presented in [48]. In this paper, the authors implemented a two-step tracking algorithm, which uses a decision tree to find the The reason for sometimes seeing the tracked object over the measurement and other times the measurement over the tracked object is due to the ADTF viewer [50].…”

Section: Data Association and Tracking Of The 16 L Lidar Objectsmentioning

confidence: 99%

“…Furthermore, two motion models, the constant velocity and the constant turn rate and velocity models, are used and combined to solve the motion uncertainty issue. One of the main downsides to the association method used in [48] is that it does not exploit all appearance information that could be gathered for an instance, and motion information is not exploited at all. Furthermore, to find the best global solution, in the current paper we have used a cost-based approach and extracted the best associations by using an optimization algorithm called the Munkres assignment algorithm.…”

Section: Data Association and Tracking Of The 16 L Lidar Objectsmentioning

confidence: 99%

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Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation

Muresan

Giosan

Nedevschi

2020

Sensors

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The stabilization and validation process of the measured position of objects is an important step for high-level perception functions and for the correct processing of sensory data. The goal of this process is to detect and handle inconsistencies between different sensor measurements, which result from the perception system. The aggregation of the detections from different sensors consists in the combination of the sensorial data in one common reference frame for each identified object, leading to the creation of a super-sensor. The result of the data aggregation may end up with errors such as false detections, misplaced object cuboids or an incorrect number of objects in the scene. The stabilization and validation process is focused on mitigating these problems. The current paper proposes four contributions for solving the stabilization and validation task, for autonomous vehicles, using the following sensors: trifocal camera, fisheye camera, long-range RADAR (Radio detection and ranging), and 4-layer and 16-layer LIDARs (Light Detection and Ranging). We propose two original data association methods used in the sensor fusion and tracking processes. The first data association algorithm is created for tracking LIDAR objects and combines multiple appearance and motion features in order to exploit the available information for road objects. The second novel data association algorithm is designed for trifocal camera objects and has the objective of finding measurement correspondences to sensor fused objects such that the super-sensor data are enriched by adding the semantic class information. The implemented trifocal object association solution uses a novel polar association scheme combined with a decision tree to find the best hypothesis–measurement correlations. Another contribution we propose for stabilizing object position and unpredictable behavior of road objects, provided by multiple types of complementary sensors, is the use of a fusion approach based on the Unscented Kalman Filter and a single-layer perceptron. The last novel contribution is related to the validation of the 3D object position, which is solved using a fuzzy logic technique combined with a semantic segmentation image. The proposed algorithms have a real-time performance, achieving a cumulative running time of 90 ms, and have been evaluated using ground truth data extracted from a high-precision GPS (global positioning system) with 2 cm accuracy, obtaining an average error of 0.8 m.

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Section: Methodsmentioning

confidence: 99%

Section: Data Association and Tracking Of The 16 L Lidar Objectsmentioning

confidence: 97%

Section: Data Association and Tracking Of The 16 L Lidar Objectsmentioning

confidence: 99%

Section: Data Association and Tracking Of The 16 L Lidar Objectsmentioning

confidence: 99%

Section: Data Association and Tracking Of The 16 L Lidar Objectsmentioning

confidence: 99%

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Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation

Muresan

Giosan

Nedevschi

2020

Sensors

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3D lidar SLAM-based systems in object detection and navigation applications

Li,

Chen,

et al. 2023

Journal of the Chinese Institute of Engineers

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Extending Reliability of mmWave Radar Tracking and Detection via Fusion With Camera

Zhang

Cao

2019

IEEE Access

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In this paper, a new radar-camera fusion system is presented. The fusion system takes into consideration the error bounds of the two different coordinate systems from the heterogeneous sensors, and further a new fusion-extended Kalman filter is utilized to adapt to the heterogeneous sensors. Real-world application considerations such as asynchronous sensors, multi-target tracking and association are also studied and illustrated in this paper. Experimental results demonstrated that the proposed fusion system can realize a range accuracy of 0.29m with an angular accuracy of 0.013rad in real-time. Therefore, the proposed fusion system is effective, reliable and computationally efficient for real-time kinematic fusion applications.

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Multimodal sparse LIDAR object tracking in clutter

Cited by 12 publications

References 24 publications

Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation

Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation

3D lidar SLAM-based systems in object detection and navigation applications

Extending Reliability of mmWave Radar Tracking and Detection via Fusion With Camera

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