MulRan: Multimodal Range Dataset for Urban Place Recognition

Kim, Giseop; Park, Yeong Sang; Cho, Younghun; Jeong, Jin-Yong; Kim, Ayoung

doi:10.1109/icra40945.2020.9197298

Cited by 210 publications

(160 citation statements)

References 30 publications

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“…For radar-based place recognition, Kim et al (2020) extended the lidar-based handcrafted representation (Kim and Kim, 2018) to radar data directly. In Sȃftescu et al (2020), NetVLAD (Arandjelovic et al, 2016) was used to achieve radar-to-radar (R2R) place recognition.…”

Section: Radar-based Mapping and Localizationmentioning

confidence: 99%

“…With the built lidar submaps L t and the radar scan R t collected at the same pose, we first use the ScanContext (Kim and Kim, 2018;Kim et al, 2020) to extract the representations S L and S R , respectively. Specifically, for radar scan, the ScanContext is the polar representation essentially.…”

Section: Signature Generationmentioning

confidence: 99%

“…We run six epochs with the Adam optimizer (Kingma and Ba, 2015) and a decayed learning rate from 0.001. We conduct the experiments on two public datasets, the Oxford Radar RobotCar (RobotCar) dataset (Maddern et al, 2017;Barnes et al, 2020a) and the Multimodal Range (MulRan) dataset (Kim et al, 2020). Both these datasets use the Navtech FMCW radar but the 3D lidar sensors use different ones, double Velodyne HDL-32E and one Ouster OS1-64.…”

Section: Implementation and Experimental Setupmentioning

confidence: 99%

“…The proposed joint learning-based method is compared to other two competitive methods. First, the ScanContext is used for comparison, which achieves not only 3D lidar place recognition (Kim and Kim, 2018) but also 2D radar place recognition in the recent publication (Kim et al, 2020). Secondly, the DiSCO The bold values are the biggest number in each column respectively, which means that one method achieves better performance compared to the other two methods for L2L, R2R or R2L.…”

Section: Multi-session: Global Localizationmentioning

confidence: 99%

“…As for lidar (Light Detection and Ranging)-based perception (Elhousni and Huang, 2020), the lidar scanner does not suffer from the illumination variations and provides precise measurements of the surrounding environments. But in adverse conditions, fog and strong light etc., or in highly dynamic environments, the lidar data are affected badly by low reflections (Carballo et al, 2020) or occlusions (Kim et al, 2020). Compared to the vision or lidar, radar sensor is naturally lighting and weather invariant and has been widely applied in the Advanced Driver Assistance Systems (ADAS) for object detection.…”

Section: Introductionmentioning

confidence: 99%

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Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Yin

Wang

et al. 2021

Front. Robot. AI

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Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditions. In this paper, a heterogeneous measurement based framework is proposed for long-term place recognition, which retrieves the query radar scans from the existing lidar (Light Detection and Ranging) maps. To achieve this, a deep neural network is built with joint training in the learning stage, and then in the testing stage, shared embeddings of radar and lidar are extracted for heterogeneous place recognition. To validate the effectiveness of the proposed method, we conducted tests and generalization experiments on the multi-session public datasets and compared them to other competitive methods. The experimental results indicate that our model is able to perform multiple place recognitions: lidar-to-lidar (L2L), radar-to-radar (R2R), and radar-to-lidar (R2L), while the learned model is trained only once. We also release the source code publicly: https://github.com/ZJUYH/radar-to-lidar-place-recognition.

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Section: Radar-based Mapping and Localizationmentioning

confidence: 99%

Section: Signature Generationmentioning

confidence: 99%

Section: Implementation and Experimental Setupmentioning

confidence: 99%

Section: Multi-session: Global Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Yin

Wang

et al. 2021

Front. Robot. AI

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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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ARD‐VO: Agricultural robot data set of vineyards and olive groves

Crocetti

Bellocchio

Dionigi

et al. 2023

Journal of Field Robotics

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The availability of real-world data in agricultural applications is of paramount importance to develop robust and effective robotic-based solutions for farming operations. In this application context, however, very few data sets are available to the community and for some important crops, such as grapes and olives, they are almost absent. Therefore, the aim of this paper is to introduce and release ARD-VO, a data set for agricultural robotics applications focused on vineyards and olive cultivations. Its main purpose is to provide the researchers with a real-world extensive set of data to support the development of solutions and algorithms for precision farming technologies in the aforementioned crops.ARD-VO has been collected with an unmanned ground vehicle (UGV) equipped with different heterogeneous sensors that capture information essential for robot localization and plant monitoring tasks. It is composed of sequences gathered in 11 experimental sessions between August and October 2021, navigating the UGV for several kilometers in four cultivation fields in Umbria, a central region of Italy. In addition, to highlight the utility of ARD-VO, two application case studies are presented. In the first one, the data set is used to compare the performance of simultaneous localization and mapping and odometry estimation methods using vision systems, light detection and ranging, and inertial measurement unit sensors. The second one shows how the multispectral images included in ARD-VO can be used to compute Normalized Difference Vegetation Index maps, which are crucial to monitor the crops and build prescription maps.

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MulRan: Multimodal Range Dataset for Urban Place Recognition

Cited by 210 publications

References 30 publications

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

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

ARD‐VO: Agricultural robot data set of vineyards and olive groves

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