Instantaneous Actual Motion Estimation with a Single High-Resolution Radar Sensor

Schlichenmaier, Johannes; Liang, Yan; Stolz, Martin; Waldschmidt, Christian

doi:10.1109/icmim.2018.8443553

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…According to the Ackermann steering geometry, the position of the ICR should be located on a line extending from the rear axle. By adding this constraint to (8), the full velocity can be determined in closed form [124]. The above methods predict velocity at the object level under the assumption of rigid motion.…”

Section: Velocity Estimationmentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

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With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

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Section: Velocity Estimationmentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

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“…Depending on the number of sensors and measurements, a least-squares regression is used to resolve up to three degrees of freedom of the object state, i.e., the velocity, orientation, and turn rate. Similarly, in [103], the velocity profile is used for a single-radar tracking, where linear and nonlinear motions are distinguished and in the latter case the orientation is separately estimated based on the extracted contour of all radar detections. In [125], a Gaussian process is used for the radar-based shape estimation, combined with an extended Kalman filter (EKF) for the motion state estimation, which, however, results in complex partial derivatives that are prone to linearization errors.…”

Section: Related Workmentioning

confidence: 99%

Grid-Based Object Tracking

Steyer¹

2021

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Mobile robots require an accurate environment perception to plan intelligent maneuvers and avoid collisions. This thesis presents a novel multi sensor environment estimation strategy that fully combines tracking moving objects and mapping the static environment. The basic idea is to fuse and accumulate measurement data by a dynamic occupancy grid model, whereas moving objects are extracted subsequently based on that generic low-level grid representation. Overall, this work results in a robust and consistent estimation of arbitrary objects and obstacles, which is demonstrated in the context of autonomous driving in complex unstructured environments. Contents Notations VIII Abstract XI 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Challenges of Multi-Sensor Environment Perception . . . . . . . . . . . . . 2 1.3 Main Contribution and Outline of This Work . . . . . . . . . . . . . . . . 8 2 Measurement Grid Representation and Fusion 13 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.2...

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“…Depending on the number of sensors and measurements, a least-squares regression is used to resolve up to three degrees of freedom of the object state, i.e., the velocity, orientation, and turn rate. Similarly, in [40], the velocity profile is used for a single-radar tracking, with a separate orientation estimation based on the contour of all radar detections in the case of a nonlinear motion. In [41], a Gaussian process is used for the radar-based shape estimation, combined with an extended Kalman filter (EKF) for the motion estimation, which, however, results in complex partial derivatives that are prone to linearization errors.…”

Section: Related Workmentioning

confidence: 99%

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Steyer

Lenk

Kellner

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Object tracking is crucial for planning safe maneuvers of mobile robots in dynamic environments, in particular for autonomous driving with surrounding traffic participants. Multistage processing of sensor measurement data is thereby required to obtain abstracted high-level objects, such as vehicles. This also includes sensor fusion, data association, and temporal filtering. Often, an early-stage object abstraction is performed, which, however, is critical, as it results in information loss regarding the subsequent processing steps. We present a new grid-based object tracking approach that, in contrast, is based on already fused measurement data. The input is thereby pre-processed, without abstracting objects, by the spatial grid cell discretization of a dynamic occupancy grid, which enables a generic multi-sensor detection of moving objects. On the basis of already associated occupied cells, presented in our previous work, this paper investigates the subsequent object state estimation. The object pose and shape estimation thereby benefit from the freespace information contained in the input grid, which is evaluated to determine the current visibility of extracted object parts. An integrated object classification concept further enhances the assumed object size. For a precise dynamic motion state estimation, radar Doppler velocity measurements are integrated into the input data and processed directly on the object-level. Our approach is evaluated with real sensor data in the context of autonomous driving in challenging urban scenarios.

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Instantaneous Actual Motion Estimation with a Single High-Resolution Radar Sensor

Cited by 11 publications

References 8 publications

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Grid-Based Object Tracking

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

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