Rethinking of Radar’s Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment

Wang, Yizhou; Wang, Gaoang; Hsu, Hung-Min; Liu, Hui; Hwang, Jenq‐Neng

doi:10.1109/cvprw53098.2021.00316

Cited by 39 publications

(16 citation statements)

References 22 publications

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“…Ouaknine et al (2021) and Lim et al (2021) with CARRADA and RaDICal both contained significantly more scenes along with a denser range-angle-doppler radar images and associated camera data. Lastly, Wang et al (2021) released a dataset with more complex scenes with a focus on learned object detection. These datasets, each contribute to radar-visual object detection and semantic labeling research.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, many of the datasets focus directly on autonomous vehicle navigation, outside of Leung et al (2017) which has a direct robotic application. This dataset instead targets low cost, lightweight ADAS sensors such as those used in Caesar et al (2020), Lim et al (2021), Ouaknine et al (2021), and Wang et al (2021). Of the datasets that make use of ADAS sensors, only Caesar et al (2020) is focused on navigation, and also primarily uses the radars own onboard signal processing and detection systems instead of streaming raw data.…”

Section: Introductionmentioning

confidence: 99%

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ColoRadar: The direct 3D millimeter wave radar dataset

Kramer

Kyle

Williams

et al. 2022

The International Journal of Robotics Research

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This work presents two different forms of dense, high-resolution radar data from two frequency modulated continuous wave radar sensors, along sparse radar pointclouds produced by one of the radar sensors. In addition, all datasets include 3D lidar and inertial measurements, and a lidar-based simultaneous localization and mapping pose estimation. Over 2 h of 6D pose data was generated across 52 datasets collected in highly diverse 3D environments including lab spaces, outside and inside large buildings, urban walkways, and a mine. One dataset, from the ASPEN Lab, also includes precision groundtruth generated from a motion capture system. Intrinsic radar calibration and measured extrinsic sensor position calibrations are also provided along with python based development tools to interact with the various datasets. This data is designed to assist with generating radar based localization algorithms and calibrations between radar and other sensors.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ColoRadar: The direct 3D millimeter wave radar dataset

Kramer

Kyle

Williams

et al. 2022

The International Journal of Robotics Research

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“…While they provide accurate range and velocity, they suffer from a low azimuth resolution leading to ambiguity in separating close objects. Recent datasets include processed radar representations such as the entire Range-Azimuth-Doppler (RAD) tensor [31], [43] or single views of this tensor -either Range-Azimuth (RA) [1], [38], [17], [41], [27] or Range-Doppler (RD) [27]. These representations require large bandwidth to be transmitted as well as large memory storage.…”

Section: Radar Backgroundmentioning

confidence: 99%

“…Owing to the promising capabilities of HD radars, our work [24] 2019 Small HD CL 3D Boxes RadarRobotCar [1] 2020 Large S CLO CARRADA [31] 2020 Small LD C Segmentation RADIATE [38] 2020 Medium S CLO 2D Boxes MulRan [17] 2020 Medium S CLO Zendar [27] 2020 Small HD CL 2D Boxes CRUW [41] 2021 Medium LD C Point Location RadarScenes [36] 2021 Large HD CO Point-wise RADDet [43] 2021…”

Section: Introductionmentioning

confidence: 99%

Raw High-Definition Radar for Multi-Task Learning

Rebut¹,

Ouaknine²,

Malik³

et al. 2021

Preprint

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Fig. 1: Overview of our RADIal dataset. RADIal includes a set of 3 sensors (camera, laser scanner, high-definition radar) and comes with GPS and vehicle's CAN traces; 25K synchronized samples are recorded in raw format. (a) Camera image with projected laser point cloud in red and radar point cloud in indigo, vehicle annotation in orange and free driving space annotation in green; (b) Radar power spectrum with bounding box annotations; (c) Free driving space annotation in bird-eye view, with annotated vehicle bounding boxes in orange, radar point cloud in indigo and laser point cloud in red; (d) Range-Azimuth map in Cartesian coordinates overlayed with radar point cloud and laser point cloud; (e) GPS trace in red and odometry trajectory reconstruction in green.

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“…Instead, raw data tensors and deep neural networks can be used to replace and improve traditional techniques for object detection, classification and segmentation without losing information. Recently, radar datasets and challenges such as CARRADA [5], RADDet [6] or CRUW [7], where radar data is provided as raw data tensors, have opened up research on new deep learning methods for automotive radar ranging from object detection [6], [8], [9] to object segmentation [10].…”

Section: Introductionmentioning

confidence: 99%

DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps

Colin

VanRullen

Salle

et al. 2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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Due to the small number of raw data automotive radar datasets and the low resolution of such radar sensors, automotive radar object detection has been little explored with deep learning models in comparison to camera and lidarbased approaches. However, radars are low-cost sensors able to accurately sense surrounding object characteristics (e.g., distance, radial velocity, direction of arrival, radar crosssection) regardless of weather conditions (e.g., rain, snow, fog). Recent open-source datasets such as CARRADA, RADDet or CRUW have opened up research on several topics ranging from object classification to object detection and segmentation. In this paper, we present DAROD, an adaptation of Faster R-CNN object detector for automotive radar on the range-Doppler spectra. We propose a light architecture for features extraction, which shows an increased performance compare to heavier vision-based backbone architectures. Our models reach respectively an mAP@0.5 of 55.83 and 46.57 on CARRADA and RADDet datasets, outperforming competing methods.

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Rethinking of Radar’s Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment

Cited by 39 publications

References 22 publications

ColoRadar: The direct 3D millimeter wave radar dataset

ColoRadar: The direct 3D millimeter wave radar dataset

Raw High-Definition Radar for Multi-Task Learning

DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps

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