%0 Conference Paper %1 1013-kim %A Kim, G. %A Park, Y. S. %A Cho, Y. %A Jeong, J. %A Kim, A. %B 2020 IEEE International Conference on Robotics and Automation (ICRA) %D 2020 %K dataset %P 6246-6253 %R 10.1109/ICRA40945.2020.9197298 %T MulRan: Multimodal Range Dataset for Urban Place Recognition %U https://ieeexplore.ieee.org/document/9197298/ %X This paper introduces a multimodal range dataset namely for radio detection and ranging (radar) and light detection and ranging (LiDAR) specifically targeting the urban environment. By extending our workshop paper 1 to a larger scale, this dataset focuses on the range sensor-based place recognition and provides 6D baseline trajectories of a vehicle for place recognition ground truth. Provided radar data support both raw-level and image-format data, including a set of time-stamped 1D intensity arrays and 360° polar images, respectively. In doing so, we provide flexibility between raw data and image data depending on the purpose of the research. Unlike existing datasets, our focus is at capturing both temporal and structural diversities for range-based place recognition research. For evaluation, we applied and validated that our previous location descriptor and its search algorithm 2 are highly effective for radar place recognition method. Furthermore, the result shows that radar-based place recognition outperforms LiDAR-based one exploiting its longer-range measurements. The dataset is available from https://sites.google.com/view/mulran-pr.

@inproceedings{1013-kim, abstract = {This paper introduces a multimodal range dataset namely for radio detection and ranging (radar) and light detection and ranging (LiDAR) specifically targeting the urban environment. By extending our workshop paper [1] to a larger scale, this dataset focuses on the range sensor-based place recognition and provides 6D baseline trajectories of a vehicle for place recognition ground truth. Provided radar data support both raw-level and image-format data, including a set of time-stamped 1D intensity arrays and 360° polar images, respectively. In doing so, we provide flexibility between raw data and image data depending on the purpose of the research. Unlike existing datasets, our focus is at capturing both temporal and structural diversities for range-based place recognition research. For evaluation, we applied and validated that our previous location descriptor and its search algorithm [2] are highly effective for radar place recognition method. Furthermore, the result shows that radar-based place recognition outperforms LiDAR-based one exploiting its longer-range measurements. The dataset is available from https://sites.google.com/view/mulran-pr.}, added-at = {2021-04-12T11:14:52.000+0200}, author = {{Kim}, G. and {Park}, Y. S. and {Cho}, Y. and {Jeong}, J. and {Kim}, A.}, biburl = {https://www.bibsonomy.org/bibtex/293a67d00e854fe9bd8ff885634e43da0/pkoch}, booktitle = {2020 IEEE International Conference on Robotics and Automation (ICRA)}, doi = {10.1109/ICRA40945.2020.9197298}, interhash = {93a7ea774910d1f8aa4a5b797c45fc3e}, intrahash = {93a67d00e854fe9bd8ff885634e43da0}, issn = {2577-087X}, keywords = {dataset}, month = may, pages = {6246-6253}, timestamp = {2021-04-12T11:14:52.000+0200}, title = {MulRan: Multimodal Range Dataset for Urban Place Recognition}, url = {https://ieeexplore.ieee.org/document/9197298/}, year = 2020 }