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 .

In this letter, we present a dataset capturing diverse visual data formats that target varying luminance conditions. While RGB cameras provide nourishing and intuitive information, changes in lighting conditions potentially result in catastrophic failure for robotic applications based on vision sensors. Approaches overcoming illumination problems have included developing more robust algorithms or other types of visual sensors, such as thermal and event cameras. Despite the alternative sensors’ potential, there still are few datasets with alternative vision sensors. Thus, we provided a dataset recorded from alternative vision sensors, by handheld or mounted on a car, repeatedly in the same space but in different conditions. We aim to acquire visible information from co-aligned alternative vision sensors. Our sensor system collects data more independently from visible light intensity by measuring the amount of infrared dissipation, depth by structured reflection, and instantaneous temporal changes in luminance. We provide these measurements along with inertial sensors and ground-truth for developing robust visual SLAM under poor illumination.

Achieving general 3D motion estimation for all-visibility has been a key challenge in robotics, especially in extreme environments. The widely adopted camera and LiDAR-based motion estimation critically deteriorate under fog or smoke. In this letter, we devised a unique sensor system for 3D velocity estimation by compositing two orthogonal radar sensors. The proposed configuration allowed securing returns from the static objects on the ground. This work aimed at a realistic sensor deployment in a harsh environment by casing the bare sensor rig into a plastic box. As will be shown, the proposed velocity-based ego-motion estimation presented reliable performance over existing point matching-based methods, which degraded when measurement attenuates due to the casing. Furthermore, we introduce a novel radar instant velocity factor for pose-graph simultaneous localization and mapping (SLAM) framework and solve for 3D ego-motion in the integration with IMU. The validation reveals that the proposed method can be applied to estimate general 3D motion in both indoor and outdoor, targetting various visibility and the structureness in the environment.

In urban environments, () are essential for autonomous driving. Most () methodologies have been developed for relatively static environments, despite real-world environments having many dynamic objects such as vehicles, bicycles, and pedestrians. This paper proposes an efficient and robust. Our framework leverages the estimated background model to achieve robust motion estimation in dynamic urban environments. Based on probabilistic object estimation, the dynamic removal module estimates a nonparametric background model to recognize dynamic objects. This module estimates the probability of the difference of the range values from the accumulated frames. Then, dynamic objects are removed by adapting the sensor velocity from the estimated ego-motion. In the local mapping module, our method optimizes the LiDAR motion considering the dynamic characteristics of LiDAR point clouds. Finally, the proposed method results in a global map with static point clouds and accurate LiDAR motion with global pose optimization. We tested the proposed method on the well-known public dataset (KITTI) and the custom dataset with complex environments, including various moving objects. Comparisons with () methods demonstrate that the our approach is more robust and efficient. For example, the proposed method performed an average $0.63\%$ and $0.18^{\circ}/100\;m$ errors on the KITTI dataset with $0.96ms$ processing time that convinces real-time processing.