Complex Urban LiDAR Data Set

Abstract

Fig. 1: This paper provides the complex urban data set including metropolitan area, apartment building complex and underground parking lot. Sample scenes from the data set can be found in https://youtu.be/IguZjmLf5V0.Abstract-This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides for maximal coverage. One 2D LiDAR faces backward while the other faces forwards to collect data of roads and buildings, respectively. Raw sensor data from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global Positioning System (GPS) are presented in a file format for vehicle pose estimation. The pose information of the vehicle estimated at 100 Hz is also presented after applying the graph simultaneous localization and mapping (SLAM) algorithm. For the convenience of development, the file player and data viewer in Robot Operating System (ROS) environment were also released via the web page. The full data sets are available at: http://irap.kaist.ac.kr/dataset. In this website, 3D preview of each data set is provided using WebGL.

RINS-W: Robust Inertial Navigation System on Wheels

“…Indeed, as illustrated on Figure 2, when the wheels of a car actually stop, the car undergoes a rotational motion 1 Without loss of generality, we assume that the body frame is aligned with the IMU frame. Real IMU data of a car stopping from sequence urban06 of [11]. We see (9) holds and thus z VEL n = 1 at t = 5.8s while (10) does not hold yet.…”

“…The following results are obtained on the complex urban LiDAR dataset [11], that consists of data recorded on a consumer car moving in complex urban environments, e.g. metropolitan areas, large building complexes and underground parking lots, see Figure 6.…”

“…For the level of precision we pursue in the present paper, distinguishing between (9) and (10) is pivotal since it allows us to: i) properly label motion profiles before training (see Section V-B, where we have different thresholds on position and on angular velocity); and: ii) improve detection accuracy since only one motion pattern can be identified as valid. (11) and (13) generally hold for robots moving indoors or cars on roads. Note that (13) is expressed in the body frame, and thus generally holds for a car moving on a road even if the road is not level.…”

“…Taking into account vehicle constraints and odometer measurements are known to increase the robustness of visual- Trajectory ground truth and estimates obtained by various methods: integration of IMU signals; odometry based on a differential wheel encoder system; odometry combined with an highly accurate and expensive Fiber optics Gyro (FoG) that provides orientation estimates; and the proposed RINS-W approach which considers only the IMU sensor embarked in the vehicle, and which outperforms the other schemes. The final distance error for this long-term sequence urban16 (73 minutes) of the car dataset [11] is 20 m for the RINS-W solution. The deep learning based detector (see Section IV-A) has of course not been trained or cross-validated on this sequence.…”

“…• we demonstrate the performances of the approach on a publicly available car dataset [11] in Section V. Our approach solely based on the IMU produces accurate estimates with a final distance w.r.t. ground truth of 20 m on the 73 minutes test sequence urban16, see Figure 1.…”