Li-Ta Hsu

3D building model-based pedestrian positioning method using GPS/GLONASS/QZSS and its reliability calculation

Abstract The current low-cost global navigation satellite systems (GNSS) receiver cannot calculate satisfactory positioning results for pedestrian applications in urban areas with dense buildings due to multipath and non-line-of-sight effects. We develop a rectified positioning method using a basic three-dimensional city building model and ray-tracing simulation to mitigate the signal reflection effects. This proposed method is achieved by implementing a particle filter to distribute possible position candidates. The likelihood of each candidate is evaluated based on the similarity between the pseudorange measurement and simulated pseudorange of the candidate. Finally, the expectation of all the candidates is the rectified positioning of the proposed map method. The proposed method will serve as one sensor of an integrated system in the future. For this purpose, we successfully define a positioning accuracy based on the distribution of the candidates and their pseudorange similarity. The real data are recorded at an urban canyon environment in the Chiyoda district of Tokyo using a commercial grade u-blox GNSS receiver. Both static and dynamic tests were performed. With the aid of GLONASS and QZSS, it is shown that the proposed method can achieve a 4.4-m 1σ positioning error in the tested urban canyon area.

GPS Error Correction With Pseudorange Evaluation Using Three-Dimensional Maps

The accuracy of the positions of a pedestrian is very important and useful information for the statistics, advertisement, and safety of different applications. Although the GPS chip in a smartphone is currently the most convenient device to obtain the positions, it still suffers from the effect of multipath and nonline-of-sight propagation in urban canyons. These reflections could greatly degrade the performance of a GPS receiver. This paper describes an approach to estimate a pedestrian position by the aid of a 3-D map and a ray-tracing method. The proposed approach first distributes the numbers of position candidates around a reference position. The weighting of the position candidates is evaluated based on the similarity between the simulated pseudorange and the observed pseudorange. Simulated pseudoranges are calculated using a ray-tracing simulation and a 3-D map. Finally, the proposed method was verified through field experiments in an urban canyon in Tokyo. According to the results, the proposed approach successfully estimates the reflection and direct paths so that the estimate appears very close to the ground truth, whereas the result of a commercial GPS receiver is far from the ground truth. The results show that the proposed method has a smaller error distance than the conventional method.

NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models

Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, their performance in the downtown areas of cities is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes a new positioning method using 3D building models and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area. The 3D building model uses a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudoranges are inconsistent. Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.

Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment.

This research proposes an accurate vehicular positioning system which can achieve lane-level performance in urban canyons. Multiple passive sensors, which include Global Navigation Satellite System (GNSS) receivers, onboard cameras and inertial sensors, are integrated in the proposed system. As the main source for the localization, the GNSS technique suffers from Non-Line-Of-Sight (NLOS) propagation and multipath effects in urban canyons. This paper proposes to employ a novel GNSS positioning technique in the integration. The employed GNSS technique reduces the multipath and NLOS effects by using the 3D building map. In addition, the inertial sensor can describe the vehicle motion, but has a drift problem as time increases. This paper develops vision-based lane detection, which is firstly used for controlling the drift of the inertial sensor. Moreover, the lane keeping and changing behaviors are extracted from the lane detection function, and further reduce the lateral positioning error in the proposed localization system. We evaluate the integrated localization system in the challenging city urban scenario. The experiments demonstrate the proposed method has sub-meter accuracy with respect to mean positioning error.

GNSS/Onboard Inertial Sensor Integration With the Aid of 3-D Building Map for Lane-Level Vehicle Self-Localization in Urban Canyon

Lane-level vehicle self-localization is a challenging and significant issue arising in autonomous driving and driver-assistance systems. The Global Navigation Satellite System (GNSS) and onboard inertial sensor integration are among the solutions for vehicle self-localization. However, as the main source in the integration, GNSS positioning performance is severely degraded in urban canyons because of the effects of multipath and non-line-of-sight (NLOS) propagations. These GNSS positioning errors also decrease the performance of the integration. To reduce the negative effects caused by GNSS positioning error, this paper proposes to employ an innovative GNSS positioning technique with the aid of a 3-D building map in the integration. The GNSS positioning result is used as an observation, and this is integrated with the information from the onboard inertial sensor and vehicle speedometer in a Kalman filter framework. To achieve stable performance, this paper proposes to evaluate and consider the accuracy of the employed GNSS positioning method in dynamic integration. A series of experiments in different scenarios is conducted in an urban canyon, which can demonstrate the effectiveness of the proposed method using various evaluation and comparison processes.

Urban Pedestrian Navigation Using Smartphone-Based Dead Reckoning and 3-D Map-Aided GNSS

This paper focuses on the pedestrian navigation in highly urbanized area, where a current smartphone and a commercial global navigation satellite system (GNSS) receiver perform poorly because of the reflection and blockage of GNSS signal by buildings and foliage. A 3-D map-aided pedestrian positioning method is previously developed to mitigate and correct the multipath GNSS signal. However, it still suffers from the low availability due to the insufficient number of satellites. We develop a smartphone-based pedestrian dead reckoning (PDR) algorithm, which is carried in the pedestrians trousers. This PDR is capable of not only providing continues solutions but also indicating the pedestrian motions. A closedloop Kalman filter with adaptive tuning is proposed to integrate the 3-D map-aided GNSS method with the smartphone-based PDR system. According to the experiment results, the proposed integration system can achieve ~1.5and 5.5-m of positioning errors in a middle-class and deep urban canyon, respectively.

Vehicle self-localization in urban canyon using 3D map based GPS positioning and vehicle sensors

Precise and robust vehicle localization in the urban canyon is a new challenge arising in the autonomous driving and driver assistance systems. Sensor integration is proposed to realize this target in his paper. Global Positioning System (GPS) has been proven itself reliable for accurate vehicle self-localization in the open sky scenario. However, it suffers from the effect of multipath and Non-Line-Of-Sigh (NLOS) propagation in urban canyon. The paper proposes to estimate vehicle position by using 3-dimensional (3D) map and ray-racing method in order to overcome the problems in urban canyon. The proposed positioning method distributes numbers of positioning candidates around of reference positioning, and then the weighing of the position candidates are evaluated based on the similarity between the simulated pseudorange and the observed pseudorange. In his way, the additional 3D map information is used to reduce the effect of multipath and NLOS. Moreover, the information from vehicle sensors, including motion sensor and rotation sensor, are integrated with he GPS positioning result in a Kalman filer framework. The integration no only smoothens the trajectory of vehicle, but also reduces the positioning error. The experimental results demonstrate the accuracy of our proposed method and is feasibility for autonomous driving.

Probability estimation for pedestrian crossing intention at signalized crosswalks

With the rapid development of the techniques for autonomous driving and ADAS in the last decade, more advanced methods to understand pedestrian behavior are required. Crosswalks at intersections are the one of most hazardous where many accidents between turning-vehicles and pedestrians occur. In this paper, we present a method for estimating the pedestrians intention to cross a signalized crosswalk or stop in front of it. The intention is crucial to not only the collision avoidance but also smooth traffic in the context of autonomous driving by reducing unnecessary risk margins. Regarding the behavioral flow of pedestrian: assessment, decision-making and physical movement, as a stochastic process, we construct a probabilistic model with the Dynamic Bayesian Network. It takes account of not only pedestrian physical states but also contextual information and integrates the relationship among them. By employing the particle filter as a Bayesian filtering framework, the model estimates the pedestrian state from signal information and pedestrian position measurements. Evaluation using experimental data collected in real traffic scene shows that the proposed model has an ability to detect the pedestrian intention to cross a crosswalk even when he/she is far from it.

Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons

Sensors play important roles for autonomous driving. Localization is definitely a key one. Undoubtedly, global positioning system (GPS) sensor will provide absolute localization for almost all the future land vehicles. In terms of driverless car, 1.5 m of positioning accuracy is the minimum requirement, since the vehicle has to keep in a driving lane that usually wider than 3 m. However, the skyscrapers in highly-urbanized cities, such as Tokyo and Hong Kong, dramatically deteriorate GPS localization performance, leading more than 50 m of error. GPS signals are reflected at modern glassy buildings, which caused the notorious multipath effect. Fortunately, the number of navigation satellite is rapidly increasing in a global scale, since the rise of multi-global navigation satellite system. It provides an excellent opportunity for positioning algorithm developer of GPS sensor. More satellites in the sky imply more measurements to be received. Novelty, this paper proposes to take advantage of the fact that clean measurements (refers to line-of-sight measurement) are consistent and multipath measurements are inconsistent. Based on this consistency check, the faulty measurements can be detected and excluded to obtain better localization accuracy. Experimental results indicate that the proposed method can achieve less than 1-m lateral positioning error in middle urban canyons.

Optimization of 3D building models by GPS measurements

Recently, 3D building models have become an important aid to many positioning methods such as LiDAR and GPS positioning. Creating an accurate 3D building model requires accurate 2D building boundaries. We propose a method to correct the horizontal location errors of the 3D building model using GPS measurements. In an urban canyon, several GPS signals are reflected by buildings, and these reflections are potentially capable of indicating the correct position of the buildings. Starting with a raw 3D building model, we apply a signal ray tracing method to track the simulated reflection path of the GPS signal. Theoretically, the length of observed reflection path, which is known as the non-line-of-sight pseudorange, and the length of simulated reflection path should be similar. However, if the 3D map is not accurate, a difference between the pseudorange and simulated range is found. Using this difference, the proposed method estimates the true position of the wall of the 3D map. Results show that the proposed method successfully corrects the position of the wall of the raw 3D map and achieves sub-meter accuracy.