Complex urban dataset with multi-level sensors from highly diverse urban environments

By Jinyong Jeong, Younggun Cho, Young-sik Shin, Hyunchul Roh, Ayoung Kim. Int. J. Robotics Res., 2019

309 citations recorded by the source corpus.

The high diversity of urban environments, at both the inter and intra levels, poses challenges for robotics research. Such challenges include discrepancies in urban features between cities and the deterioration of sensor measurements within a city. With such diversity in consideration, this paper aims to provide Light Detection and Ranging (LiDAR) and image data acquired in complex urban environments. In contrast to existing datasets, the presented dataset encapsulates various complex urban features and addresses the major issues of complex urban areas, such as unreliable and sporadic Global Positioning System (GPS) data, multi-lane roads, complex building structures, and the abundance of highly dynamic objects. This paper provides two types of LiDAR sensor data (2D and 3D) as well as navigation sensor data with commercial-level accuracy and high-level accuracy. In addition, two levels of sensor data are provided for the purpose of assisting in the complete validation of algorithms using consumer-grade sensors. A forward-facing stereo camera was utilized to capture visual images of the environment and the position information of the vehicle that was estimated through simultaneous localization mapping (SLAM) are offered as a baseline. This paper presents 3D map data generated by the SLAM algorithm in the LASer (LAS) format for a wide array of research purposes, and a file player and a data viewer have been made available via the Github webpage to allow researchers to conveniently utilize the data in a Robot Operating System (ROS) environment. The provided file player is capable of sequentially publishing large quantities of data, similar to the rosbag player. The dataset in its entirety can be found at http://irap.kaist.ac.kr/dataset.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/08682f0517cdd61b233c1614c48b6865805015a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Environmental Engineering

In Geology

Citations in the corpus

MulRan: Multimodal Range Dataset for Urban Place Recognition (2020, methodology): “We first set a vehicle base’s coordinate, the same as in [7].”
DSEC: A Stereo Event Camera Dataset for Driving Scenarios (2021, methodology): “Oxford RobotCar [27] and KAIST Urban [28] target localization and mapping, while DDAD [29] and DrivingStereo [6] are specialized for monocular or stereo depth estimation respectively.”
Deep Multimodal Data Fusion (2024, background): “…Depth V1 and V2 [130, 161], RobotPKU [105], B3DO [71], BIWI [127], Object Detection/ Semantic Segmentation RGB+LiDAR KITTI [49], Urban [17], KAIST [72], RobortCar [114], US3D [40] College [146], H3D [84] Human Action Recognition RGB + Inertial Sensors MHAD [133], C-MHAD [192], UTD-MHAD [20],…”