Probabilistic ego-motion estimation using multiple automotive radar sensors

Rapp, Matthias; Barjenbruch, Michael; Hahn, Markus; Dickmann, Jürgen; Dietmayer, Klaus

doi:10.1016/j.robot.2016.11.009

Cited by 34 publications

(25 citation statements)

References 9 publications

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“…In the second step, to find the optimal rotational angle and translation, the sum of squared distances between the points is used [135]. Normal Distribution Transformation (NDT) scan matching [89], [134] requires a grid of probability functions created from a map for matching.…”

Section: Analytical Modelingmentioning

confidence: 99%

“…Other grey box models include Shadow RTI [150], triangulation, angle/time model [145], RSS Series Analysis (RSA) model [153], velocity model [161], Rician model [90], and MLE [134], [152]. Shadow RTI [150] measures link RSS attenuation changes across a network of millimeter wave transceivers.…”

Section: Analytical Modelingmentioning

confidence: 99%

“…The model in [152] uses reference data and a likelihood function to return a data estimate that maximizes the likelihood function value. Joint spatial and Doppler-based ego-motion estimation in [134] computes state PDFs for a vehicle's yaw rate, longitudinal velocity, and lateral velocity by using a joint optimization problem. Mean velocity [87], [161] is measured with a set of equations and pulse-pair phase estimation, which relies on a correlation product that depends on the input data.…”

Section: Analytical Modelingmentioning

confidence: 99%

“…The authors in [47], [56], [145] compare several self-created models to each other. Authors in [46], [55], [72], [89], [94], [96], [114], [127], [132], [134], [135], [143], [185] incorporate different processes in their millimeter wave sensing application pipeline that use a distinct model. Several papers use pipelines that include a tree or sequential set of multiple models coming from both the data and model driven paradigms.…”

Section: White Box Grey Box Shallow Black Box Deep Black Boxmentioning

confidence: 99%

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Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.

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Section: Analytical Modelingmentioning

confidence: 99%

Section: Analytical Modelingmentioning

confidence: 99%

Section: Analytical Modelingmentioning

confidence: 99%

Section: White Box Grey Box Shallow Black Box Deep Black Boxmentioning

confidence: 99%

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Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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“…This joint spatial and Doppler-based estimation functions without lever-arm offsets or motion assumptions but involves significant computational costs. In subsequent research [12], the normal distribution transform algorithm [16] was utilized for faster spatial alignment, and the complexity was further reduced by deriving a sparse probabilistic representation [13]. A hybrid approach [14] was proposed to decouple translational and rotational motion by combining the benefits from scan matching and instantaneous approaches.…”

Section: Related Workmentioning

confidence: 99%

Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

et al. 2021

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Attention mechanisms have been widely integrated with various neural networks to boost performance. However, when an attention mechanism was applied to a radar ego-velocity estimation network, the importance of carefully handling the amplitude and phase of complex-valued tensor was revealed. Therefore, in this study, we present a self-attention mechanism designed to handle complexvalued tensors in order to capture the rich contextual relationships implied within amplitude and phase. To exploit the advantages of complex-valued attention (CA), we evaluated its impact while performing egovelocity estimation tasks based on radar data, whose amplitude and phase are related to the electromagnetic scattering of the target being observed. Radars are suitable sensors for such tasks as they are capable of long-range detection and instantaneous velocity measurement under variable weather and lighting conditions. In particular, we coupled our CA module with complex-valued neural networks, known to be particularly powerful for handling wave phenomena. The proposed method exhibits robust estimation performance, regardless of whether the Doppler ambiguity problem occurs and eliminates the dependence on preprocessing stages, including target detection and static target indication. Furthermore, it achieves improved stability during training via geometrical constraint regularization, and implicitly allows velocity conversion between the sensor and vehicle frames, even if the mount information of the sensor was not provided. Finally, ablation experiments conducted on extensive real-world datasets show noticeable improvement in estimation performance. INDEX TERMS Automotive radar; complex-valued attention; complex-valued neural network; deep learning; ego-velocity estimation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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