Interesting areas in radar gridmaps for vehicle self-localization

Werber, Klaudius; Klappstein, Jens; Dickmann, Jürgen; Waldschmidt, Christian

doi:10.1109/icmim.2016.7533932

Cited by 17 publications

(10 citation statements)

References 3 publications

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“…Feature extraction is a fundamental task in radar egomotion estimation systems. The traditional visual localization techniques such as amplitude grid maps are investigated in literature [13], [14]. [13] uses the amplitude grid maps to transform the radar scans into grayscale images and applies SIFT and FAST feature extractions.…”

Section: Related Workmentioning

confidence: 99%

Milli-RIO: Ego-Motion Estimation With Low-Cost Millimetre-Wave Radar

Almalioglu

Turan

et al. 2021

IEEE Sensors J.

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Robust indoor ego-motion estimation has attracted significant interest in the last decades due to the fastgrowing demand for location-based services in indoor environments. Among various solutions, frequency-modulated continuous-wave (FMCW) radar sensors in millimeter-wave (MMWave) spectrum are gaining more prominence due to their intrinsic advantages such as penetration capability and high accuracy. Single-chip low-cost MMWave radar as an emerging technology provides an alternative and complementary solution for robust ego-motion estimation, making it feasible in resource-constrained platforms thanks to low-power consumption and easy system integration. In this paper, we introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip low-cost radar and inertial measurement unit sensor to estimate six-degrees-of-freedom ego-motion of a moving radar. Detailed quantitative and qualitative evaluations prove that the proposed method achieves precisions on the order of few centimeters for indoor localization tasks.

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Section: Related Workmentioning

confidence: 99%

Milli-RIO: Ego-Motion Estimation With Low-Cost Millimetre-Wave Radar

Almalioglu

Turan

et al. 2021

IEEE Sensors J.

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“…AGMs can distinguish metal, roads, and vegetation, which has its own unique advantages. Researchers in [ 18 ] mention two ways to express interesting areas In AGMs: point-shaped areas and straight areas. The characteristics of interesting areas can be extracted through DBSCAN, MSER, or connected region, as shown in Figure 10 .…”

Section: Mmw Radar Perception Approachesmentioning

confidence: 99%

“…Then, raw radar data accumulated from multiple snapshots is used to build grid maps [ 13 , 14 ]. These representations are used to express dynamic and static environment elements and applied to many applications such as object detection and tracking [ 8 , 15 , 16 ], environment mapping, and vehicle localization [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

MMW Radar-Based Technologies in Autonomous Driving: A Review

Zhou

Yang

Jiang

et al. 2020

Sensors

103

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With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.

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“…The motion and position can be determined using radar sensors [22,23] more robustly and with fewer errors during highly dynamic manoeuvres compared to standard vehicle odometry [2]. Additionally, landmarks are often used for self localisation [2,24] by recognising prominent, strongly reflective objects in the environment.…”

Section: Self Localisation: Ego Motion and Position Estimation Using mentioning

confidence: 99%