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 is a comprehensive multi-modal dataset featuring heterogeneous radars, LiDAR, IMU, GPS, and cameras, designed to advance multi-session radar SLAM and sensor fusion in complex urban environments.
Contribution
This work introduces the first dataset combining 4D radar, spinning radar, LiDAR, and other sensors for multi-robot and multi-session SLAM research.
Findings
Enables research in multi-session and multi-robot scenarios.
Supports diverse weather and urban conditions.
Facilitates advancements in localization, mapping, and place recognition.
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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Neural Network Applications · Robotic Path Planning Algorithms
MethodsGreedy Policy Search
