journal

Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms

Li-Ta Hsu, Feng Huang, Hoi-Fung Ng, Guohao Zhang, Yihan Zhong, Xiwei Bai,, Weisong Wen

NAVIGATION: Journal of the Institute of Navigation · Jan 2023

Abstract

Accurate positioning in urban canyons remains a challenging problem. To facilitate the research and development of reliable and precise positioning methods using multiple sensors in urban canyons, we built a multisensory dataset, UrbanNav, collected in diverse, challenging urban scenarios in Hong Kong. The dataset provides multi-sensor data, including data from multi-frequency global navigation satellite system (GNSS) receivers, an inertial measurement unit (IMU), multiple light detection and ranging (lidar) units, and cameras. Meanwhile, the ground truth of the positioning (with centimeter-level accuracy) is postprocessed by commercial software from NovAtel using an integrated GNSS real-time kinematic and fiber optics gyroscope inertial system. In this paper, the sensor systems, spatial and temporal calibration, data formats, and scenario descriptions are presented in detail. Meanwhile, the benchmark performance of several existing positioning methods is provided as a baseline. Based on the evaluations, we conclude that GNSS can provide satisfactory results in a middle-class urban canyon if an appropriate receiver and algorithms are applied. Both visual and lidar odometry are satisfactory in deep urban canyons, whereas tunnels are still a major challenge. Multisensory integration with the aid of an IMU is a promising solution for achieving seamless positioning in cities. The dataset in its entirety can be found on GitHub at https://github.com/ IPNL-POLYU/UrbanNavDataset. Keywords dynamic objects, gnss raw data, gnss/ins/lidar/camera, ground truth, localization, positioning, urban canyons

HSU et al. or open areas (Levinson et al., 2011) with good sky visibility for GNSS positioning and abundant environment features for lidar units and cameras. Unfortunately, the performance of existing positioning technologies is significantly degraded in the urban scenarios of megacities, such as Hong Kong, Tokyo, and New York City, due to complex building structures, unstable illumination conditions, and excessive expected dynamic objects. Poor GNSS positioning in urban canyons: GNSS positioning is significantly degraded in urban canyons because of signal reflection

Problem Addressed

Accurate positioning in urban canyons remains a challenging problem, and existing multisensory datasets do not satisfy the requirements for developing and benchmarking reliable urban navigation algorithms.

Key Contributions

UrbanNav multisensory dataset collected in diverse challenging Hong Kong urban scenarios
Multi-frequency GNSS receivers, IMU, multiple lidars, and cameras with detailed calibration and data formats
Centimeter-level ground truth postprocessed from GNSS-RTK and fiber-optic gyroscope inertial integration
Benchmark results of several existing positioning methods as baselines, with the dataset open-sourced on GitHub

Conclusions

Benchmark evaluations show that GNSS can provide satisfactory results in middle-class urban canyons with an appropriate receiver and algorithms, while visual and lidar odometry are satisfactory in deep urban canyons; tunnels remain a major challenge. Multisensory integration aided by an IMU is a promising solution for seamless positioning in cities. Planned future work includes a website for uploading results and periodic expert assessment of state-of-the-art positioning algorithms.