HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-session Radar SLAM
Hanjun Kim, Minwoo Jung, Chiyun Noh, Sangwoo Jung, Hyunho Song, Wooseong Yang, Hyesu Jang, Ayoung Kim
TL;DR
HeRCULES addresses the scarcity of heterogeneous radar data for robust SLAM by introducing a multi-sensor, multi-session benchmark that combines 4D radar, spinning radar, FMCW LiDAR, IMU, GPS, and cameras. The dataset enables radar–LiDAR fusion SLAM and cross-sensor place recognition under varied weather and urban conditions, with ground-truth poses and ROS-compatible tools. Key contributions include a comprehensive sensor suite, detailed extrinsic calibration pipelines ($R^L_R$ and $t^L_R$), diverse environmental sequences with revisits, and baseline evaluations showing the value of heterogeneous radar fusion for odometry, mapping, and recognition. The dataset is poised to drive advances in robust autonomous navigation and sensor fusion research, providing a practical resource for multi-session, cross-sensor SLAM benchmarks.
Abstract
Recently, radars have been widely featured in robotics for their robustness in challenging weather conditions. Two commonly used radar types are spinning radars and phased-array radars, each offering distinct sensor characteristics. Existing datasets typically feature only a single type of radar, leading to the development of algorithms limited to that specific kind. In this work, we highlight that combining different radar types offers complementary advantages, which can be leveraged through a heterogeneous radar dataset. Moreover, this new dataset fosters research in multi-session and multi-robot scenarios where robots are equipped with different types of radars. In this context, we introduce the HeRCULES dataset, a comprehensive, multi-modal dataset with heterogeneous radars, FMCW LiDAR, IMU, GPS, and cameras. This is the first dataset to integrate 4D radar and spinning radar alongside FMCW LiDAR, offering unparalleled localization, mapping, and place recognition capabilities. The dataset covers diverse weather and lighting conditions and a range of urban traffic scenarios, enabling a comprehensive analysis across various environments. The sequence paths with multiple revisits and ground truth pose for each sensor enhance its suitability for place recognition research. We expect the HeRCULES dataset to facilitate odometry, mapping, place recognition, and sensor fusion research. The dataset and development tools are available at https://sites.google.com/view/herculesdataset.