Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method to improve computing efficiency. As the Manhattan world is abundant in the man-made environment, this regular world can use structural features to encode the orthogonality and parallelism concealed in the building to eliminate the accumulated rotation error. We define a structural feature as an orthogonal basis composed of three orthogonal vanishing points in the Manhattan world. Meanwhile, to extract structural features in real-time on the mobile phone, we propose a fast structural feature extraction method based on the known vertical dominant direction. Our experiments on the public datasets and self-collected dataset show that our system is superior to most existing open-source systems, especially in the situations where the images are texture-less, dark, and blurry.
Digital terrestrial multimedia broadcasting (DTMB) signals have been proven as good signals of opportunity (SoP) for positioning due to their stronger transmission power, while micro-electro-mechanical systems (MEMS) sensors are feasible to couple with wireless signals to achieve good performance of positioning. Studies have found that neurons in the hippocampus and the entorhinal cortex are the "GPS" system in the brain, which can help animals navigate in large and complex environments. To achieve stable and intelligent positioning, a neuro-inspired positioning system named MM-NeuroPos is proposed to integrate MEMS sensors and DTMB signals. In specific, MEMS sensors in the smartphone are used to acquire self-motion cues, and a DTMB signals receiver is developed to perceive external ranging cues. Then, the self-motion cues and external ranging cues are integrated into the multi-type and multi-scale navigation cells model, including grid cells, head direction cells, speed cells, and distance cells. Finally, the optimal position is decoded from these different types of multi-scale cells by maximum probability optimization to realize the construction of the experience map. Field test results showed that the 68% positioning errors of the MM-NeuroPos system are 1.19 m, while errors of the self-motion cues only are 1.91 m, and the errors of the classical particle filter (PF) system are 1.65 m. This proves that the MM-NeuroPos system based on multi-type and multi-scale neuron model has superior positioning performance than the PF system.
Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong penetration and stable transmission over a large range. To improve the positioning performance of PDR, this paper proposes a robust PDR integrating magnetic field signals and DTMB signals. In our study, the Spiking Neural Network (SNN) is first used to learn the magnetic field signals of the environment, and then the learning model is used to detect the magnetic field landmarks. At the same time, the DTMB signals are collected by the self-developed signal receiver, and then the carrier phase ranging of the DTMB signals is realized. Finally, robust pedestrian positioning is achieved by integrating position information from magnetic field landmarks and ranging information from DTMB signals through Extended Kalman Filter (EKF). We have conducted indoor and outdoor field tests to verify the proposed method, and the outdoor field test results showed that the positioning error cumulative distribution of the proposed method reaches 2.84 m at a 68% probability level, while that of the PDR only reaches 8.77 m. The proposed method has been validated to be effective and has good positioning performance, providing an alternative solution for seamless indoor and outdoor positioning.
Abstract. The precision location-based services in complex environment is a challenge in the field of navigation and positioning. With the continuous development of wireless communication technology in recent years, cellular network signals such as LTE and 5G have emerged as unique advantages in navigation and positioning applications. This paper presents a time-of-arrival (TOA) estimation method based on machine learning, which can use cellular network signals to obtain accurate ranging results in low signal-to-noise ratio conditions. For this purpose, we first present the cellular network signals that can be applied in navigation and positioning. Then, we describe in detail the process of TOA estimation based on machine learning. Finally, we carried out vehicular experiments in an urban environment to test the performance of the proposed method. The test results demonstrate the feasibility of the proposed method and achieve metre-level ranging accuracy.
Global navigation satellite system (GNSS) is one of the most effective means for landslide monitoring. At present, most studies on GNSS-based landslide monitoring focus on the long-term landslide analysis, while short-term landslide displacement is not clear. The purpose of this article is to explore a short-term displacement detection method based on GNSS kinematic positioning for landslide monitoring. The significance and feasibility of short-term landslide monitoring are presented, and a short-term displacement detection method based on GNSS kinematic positioning time series segmentation is proposed. The coordinate time series is reconstructed by the Daubechies wavelet to extract the abrupt components. The detection window is formed by the current epoch coordinates and the previous epochs' coordinates and segmented according to the segmentation index. The segmentation point obtained by segmenting the detection window is regarded as a possible change point (PCP), and a test is conducted to determine whether the segmentation point is a change point (CP). Simulation and field experiments were carried out to verify the proposed method. The results show the feasibility and effectiveness of the method for short-term landslide change detection. The influence of the detection window size and segmentation index on the proposed method is discussed, and suggestions for the selection of detection window size as well as segmentation index are given.
Location-based service (LBS) has been playing an essential role in various sectors of society. As the most important solution in LBS, the Global Navigation Satellite System (GNSS) has been limited in availability in areas such as city and canyon. With the development of wireless communication technology, the widely distributed mobile networks can provide numerous quality line-of-sight path signals in GNSS-denied environments. Therefore, this paper develops a machine learning location (MLLoc) system based on receiver autonomous integrity monitoring (RAIM) to fuse mobile network signals with GNSS, in which a machine learning (ML) method is used to obtain stable time of arrival (TOA) estimation from the downlink broadcast signal of mobile networks. The field tests carried out in Long Term Evolution networks have verified the performance of the method. Specifically, the range accuracy of the TOA estimation based on ML and the positioning performance of the MLLoc system were evaluated through field tests. Our results verified that the developed MLLoc system is highly available in GNSS-denied environments and achieves meter-level positioning accuracy.
Low earth orbit (LEO) satellite navigation signal can be used as an opportunity signal in case of a Global navigation satellite system (GNSS) outage, or as an enhancement means of traditional GNSS positioning algorithms. No matter which service mode is used, signal acquisition is the prerequisite of providing enhanced LEO navigation service. Compared with the medium orbit satellite, the transit time of the LEO satellite is shorter. Thus, it is of great significance to expand the successful acquisition time range of the LEO signal. Previous studies on LEO signal acquisition are based on simulation data. However, signal acquisition research based on real data is very important. In this work, the signal characteristics of LEO satellite: power space density in free space and the Doppler shift of LEO satellite are individually studied. The unified symbol definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given. To verify these algorithms for LEO signal acquisition, a software-defined receiver (SDR) is developed. The performance of those integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite. The experimental results show that the integration strategy can expand the successful acquisition time range, and it will not expand indefinitely with the integration duration.
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