Research roundup: Navigating urban environments

June 26, 2024  - By

GNSS researchers presented hundreds of papers at the 2023 Institute of Navigation (ION) GNSS+ conference, which took place Sept. 11-15, 2023, in Denver, Colorado, and virtually.

The following four papers focused on ways to combat GNSS jamming and spoofing. The papers are available at

GPS World will be attending this year’s ION conference in Baltimore, Maryland on Sept. 16-20.

Photo: flashfilm / The Image Bank / Getty Images

Photo: flashfilm / The Image Bank / Getty Images

Fault free integrity of urban driverless vehicles

For positioning in urban environments, systems can be integrated with an inertial navigation system (INS) to help provide continuous navigation through GNSS signal outages. Besides GNSS, another approach for positioning in urban environments is feature-matching. For example, light detection and ranging (lidar) can measure distances and angles for environmental features, such as local landmarks, which can then be associated with known feature locations stored in an onboard database.

This paper investigates how GNSS and INS, when augmented by lidar ranging from local landmarks, can offer safe navigation through a real-world urban environment under fault-free assumptions to achieve 100% availability of fault-free integrity, with requirements corresponding to maximum standard deviations between 0.05 m and
0.1 m in both lateral and longitudinal directions. The team determined which system elements and parameters are the most critical to urban navigation performance, including individual INS noise parameter specifications, average vehicle speed, kinematic constraints, landmark density, integrity requirements and the effects of velocity updates.

The team simulated GNSS availability along a 9 km urban transect in downtown Chicago. They considered multi-sensor integrated navigation architectures consisting of INS, ZUPT, GNSS, lidar, WSSs, and NHL and HL kinematic constraints to improve navigation availability. The simulation involved developed measurement models and a tightly coupled INS/multi-sensor integration scheme using an extended Kalman filter (EKF).

The results revealed that the accelerometer and gyroscope random walks contribute to the total position error considerably more than the accelerometer and gyroscope drift for the driverless vehicle application, especially when the vehicle is moving at low speeds. Intentional vehicle stops with ZUPT inputs mitigate the error propagation but increase drive time. Velocity updates from WSSs can partially calibrate along-track position errors but do not completely reset the INS drifting position errors. Position reference updates are required to handle the concentrated succession of GNSS-denied conditions in the Chicago transect.

Kana Nagai, Matthew Spenko, Ron Henderson and Boris Pervan;“Fault-free integrity of urban driverless vehicle navigation with multi-sensor integration: A case study in downtown Chicago.”

3D vision-aided GNSS

In this work, researchers aim to solve the major problem of GNSS/RTK positioning for autonomous systems through a deep exploration of the relationship between GNSS satellite measurements and visual landmarks in urban canyons. A 3D vision-aided method was proposed to improve GNSS real-time kinematic (RTK) positioning. The effectiveness was verified through several challenging data sets collected in urban canyons of Hong Kong using low-cost automobile-level GNSS receivers together with an automobile visual/inertial sensor suite.

To mitigate the impact of reflected non-line-of-sight (NLOS) reception, a sky-pointing camera with a deep neural network was employed to exclude these measurements. However, NLOS exclusion results in distorted satellite geometry. To fill this gap, complementarity between the low-lying visual landmarks and the high-elevation satellite measurements was explored to improve the geometric constraints. Specifically, inertial measurement units (IMUs), visual landmarks captured by a forward-looking camera, and healthy GNSS measurements were tightly integrated to estimate the GNSS-RTK float solution. The integer ambiguities and the fixed GNSS-RTK solution were then resolved. The effectiveness of the proposed method was verified using several data sets collected in urban canyons in Hong Kong.

The research indicated that GNSS-RTK promises potential solutions that may provide accurate, cost-effective, and drift-free positioning services for autonomous systems with specific navigation requirements. Unfortunately, the performance of the GNSS-RTK is significantly challenged in urban canyons due to the poor quality of GNSS measurements and satellite geometric distributions caused by signal blockage and reflections from surrounding buildings.

Weisong Wen, Xiwei Bai, and Li-Ta Hsu; “3D vision aided GNSS real-time kinematic positioning for autonomous systems in urban canyons.”

Low-cost inertial aids for GNSS

The rise of connected and automated vehicles has created a need for robust globally referenced positioning with increasing accuracy. Carrier-phase differential GNSS (CDGNSS) — a real-time variant for mobile platforms commonly known as real-time kinematic (RTK) GNSS — is a centimeter-accurate positioning technique that differences a receiver’s GNSS observables with those from a nearby fixed reference station to eliminate most sources of measurement error.

In this paper, researchers expand the navigation filter component of the CDGNSS system by tightly coupling with an inertial sensor and with vehicle dynamics constraints, and by incorporating measurements from multiple vehicle-mounted GNSS antennas. It also develops a novel robust estimation technique to mitigate the effects of multipath and allow for graceful recovery from incorrect integer fixes.

The estimator was evaluated using the publicly available TEX-CUP urban positioning data set, yielding a 96.6% and 97.5% integer fix availability, and a 12 cm and 10 cm overall (fix and float) 95th-percentile horizontal positioning error with a consumer-grade and industrial-grade inertial sensor, respectively, over more than two hours of driving in the urban core of Austin, Texas.

A performance sensitivity analysis showed that the false-fix detection and recovery scheme is key to achieving an acceptably low false integer fixing rate of 0.3% and 0.4%, respectively. Having a second vehicle-mounted GNSS antenna significantly increased integer-fix availability, decreased false-fix rate, and improved both root-mean-square and 95th-percentile positioning performance as compared to a single-baseline CDGNSS configuration.

James E. Yoder and Todd E. Humphreys; “Low-cost inertial aiding for deep-urban tightly coupled multi-antenna precise GNSS.”

Benchmarking urban navigation algorithms

In this work, to facilitate the research and development of reliable and precise positioning methods using multiple sensors in urban canyons, the research team built a multisensory dataset, UrbanNav, collected in diverse, challenging urban scenarios in Hong Kong. The dataset provided multi-sensor data, including data from multi-frequency GNSS receivers, an 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.

Detailed presentations are provided for sensor systems, spatial and temporal calibration, data formats, and scenario descriptions. Also, the benchmark performance of several existing positioning methods is included as a baseline.

Based on the evaluations, the team concluded 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.

Li-Ta Hsu, Feng Huang, Hoi-Fung Ng, Guohao Zhang, Yihan Zhong, Xiwei Bai, and Weisong Wen; “Hong Kong UrbanNav: An open-source multisensory dataset for benchmarking urban navigation algorithms.”