RADIATE: A Radar Dataset for Automotive Perception in Bad Weather

Sheeny, Marcel; Pellegrin, Emanuele De; Mukherjee, Saptarshi; Ahrabian, Alireza; Wang, Sen; Wallace, Andrew M.

doi:10.1109/icra48506.2021.9562089

Cited by 145 publications

(102 citation statements)

References 20 publications

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“…So far there exist three public datasets that provide longrange radar data with dense returns: the Oxford RobotCar Radar Dataset [57], [58], the MulRan Dataset [59] and the RADIATE Dataset [60]. We choose the Oxford RobotCar and MulRan datasets for detailed quantitative benchmarking and our RADIATE dataset mainly for qualitative evaluation in our experiments.…”

Section: B Datasetsmentioning

confidence: 99%

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Radar SLAM: A Robust SLAM System for All Weather Conditions

Hong¹,

Pétillot²,

Wallace³

et al. 2021

Preprint

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A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade their imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensing geometry and noise characteristics bring open challenges when being exploited for SLAM. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion tracking algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions, e.g. heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.

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Section: B Datasetsmentioning

confidence: 99%

“…3) RADIATE Dataset. : The RADIATE dataset is our recently released dataset which includes radar, LiDAR, stereo camera and GPS/IMU [60]. One of its unique features is that it provides data in extreme weather conditions, such as rain and snow, as shown Fig.…”

Section: ) Oxford Robotcar Radar Datasetmentioning

confidence: 99%

Radar SLAM: A Robust SLAM System for All Weather Conditions

Hong¹,

Pétillot²,

Wallace³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

“…However, it is a limited processed representation and it is biased to the signal processing pipeline. Several other datasets use a 360 • scanning radar [1], [38], [17]. However, its angular resolution is limited as with traditional radars and it does not provide Doppler information.…”

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%

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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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A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

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Long‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

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RADIATE: A Radar Dataset for Automotive Perception in Bad Weather

Cited by 145 publications

References 20 publications

Radar SLAM: A Robust SLAM System for All Weather Conditions

Radar SLAM: A Robust SLAM System for All Weather Conditions

Raw High-Definition Radar for Multi-Task Learning

A systematic literature review on long‐term localization and mapping for mobile robots

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