Instantaneous ego-motion estimation using Doppler radar

Kellner, Dominik; Barjenbruch, Michael; Klappstein, Jens; Dickmann, Jürgen; Dietmayer, Klaus

doi:10.1109/itsc.2013.6728341

Cited by 127 publications

(95 citation statements)

References 10 publications

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“…the car is moving along a straight line, the authors of [2] present an approach to determine the velocity vector and the orientation. But an urban scenario contains turning vehicles, and estimating their movement is a key factor.…”

Section: A Boundary Conditions For Radar Sensors and Velocity Vectormentioning

confidence: 99%

Estimation of the orientation of vehicles in high-resolution radar images

Roos¹,

Kellner²,

Klappstein³

et al. 2015

2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

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The availability of high-resolution image radars allows estimating the orientation of vehicles from a single measurement without temporal filtering. This gives the opportunity to react even faster to certain critical traffic scenes. This paper presents an approach for estimating the orientation of a vehicle. The orientated bounding box algorithm known from literature is adapted to this end and a quality function is introduced to choose the optimal bounding box. In addition, a brute-force approach for determining the best possible outcome is presented.

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Section: A Boundary Conditions For Radar Sensors and Velocity Vectormentioning

confidence: 99%

Estimation of the orientation of vehicles in high-resolution radar images

Roos¹,

Kellner²,

Klappstein³

et al. 2015

2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

Self Cite

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show abstract

“…In order to meet automotive cost targets, a compromise between HW architecture and the use of high resolution algorithms has to be found. First results in that field can be found in [20,21].…”

Section: )mentioning

confidence: 99%

“…As mentioned above, the estimation of the egovehicle's motion is a key capability for mobile robot (hence highly automated vehicles) localization. In [21] an algorithm has been proposed, that allows to instantly determine the complete 2D motion state of the ego vehicle (longitudinal, lateral velocity and yaw rate). It evaluates the relative motion between radar sensors with excellent Doppler resolution and their received stationary reflections (targets).…”

Section: )mentioning

confidence: 99%

“…The motion estimation is free of bias and drift. [21] 4) Radar Grid Localization (SLAM): In [19], a robust Monte-Carlo based localization is presented. Robustness is achieved through a stochastic analysis of previous observations of the area of interest.…”

Section: )mentioning

confidence: 99%

“…In [10,21] generation of dense Doppler point clouds using either single radars or stereo-radar configuration is described. This Doppler distribution can be used as input state in Kalman filters.…”

Section: )mentioning

confidence: 99%

See 2 more Smart Citations

"Present research activities and future requirements on automotive radar from a car manufacturer´s point of view"

Dickmann

Klappstein

Hahn

et al. 2015

2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

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Automotive Radar has already found its way into nearly all car manufacturers portfolio, even for small car platforms. Over the decades, the performance requirements increased steadily from simple detector tasks in blind spot monitoring systems to multi range smart environment perception sensors. The utmost push in performance requirement is initiated with the trend towards highly automated driving. Future automotive radar systems have to provide imaging like capabilities and have to interact in radar networks, which allow for highly comprehensive perception tasks. The paper will provide an overview on state of the art automotive radar usage on the basis of the DAIMLER car platforms, will give an outline on future requirements for highly automated driving and will present recent approaches in radar based environmental perception.

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Relocalization based on millimeter wave radar point cloud for visually degraded environments

Cheng

Pang

Jiang³

et al. 2023

Journal of Field Robotics

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Simultaneously localization and mapping (SLAM) has been widely used in autonomous mobile systems to fulfill autonomous navigation. Relocalization plays an important role in SLAM for closing the loop and eliminating the drift of pose estimation. Traditional methods mostly rely on LiDAR or camera sensors, which may degrade or even fail in rainy or dusty situations or with large illumination changes. In this article, we explore the use of low‐cost commercial millimeter wave (mmWave) radars and propose a noval mmWave radar point cloud‐based relocalization method. Our method first pre‐processes the radar point cloud and, based on that, achieves fast 3‐DOF pose estimation for the robot. We build a prototype and thoroughly evaluate our method using data sets collected by our platform in four complex environments, including street, park, road, and water surface scenarios. The experimental results show that our method consistently outperforms other baseline methods including the vision‐based counterparts, especially in the visual degraded scenes.

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Instantaneous ego-motion estimation using Doppler radar

Cited by 127 publications

References 10 publications

Estimation of the orientation of vehicles in high-resolution radar images

Estimation of the orientation of vehicles in high-resolution radar images

"Present research activities and future requirements on automotive radar from a car manufacturer´s point of view"

Relocalization based on millimeter wave radar point cloud for visually degraded environments

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