A Fusion Positioning System Based on Camera and LiDAR for Unmanned Rollers in Tunnel Construction.

As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion positioning system based on a camera and LiDAR. This system integrates loop closure detection and LiDAR odometry into the visual odometry framework. Furthermore, recognizing the prevalence of similar scenes in tunnels, we innovatively combine loop closure detection with the compaction process of rollers in fixed areas, proposing a selection method for loop closure candidate frames based on the compaction process. Through on-site experiments, it is shown that this method not only enhances the accuracy of loop closure detection in similar environments but also reduces the runtime. Compared with visual systems, in static positioning tests, the longitudinal and lateral accuracy of the fusion system are improved by 12 mm and 11 mm, respectively. In straight-line compaction tests under different illumination levels, the average lateral error increases by 34.1% and 32.8%, respectively. In lane-changing compaction tests, this system enhances the positioning accuracy by 33% in dim environments, demonstrating the superior positioning accuracy of the fusion positioning system amid illumination changes in tunnels.

An Effective GNSS/PDR Fusion Positioning Algorithm on Smartphones for Challenging Scenarios.

The location-based smartphone service brings new development opportunities for seamless indoor/outdoor positioning. However, in complex scenarios such as cities, tunnels, overpasses, forests, etc., using only GNSS on smartphones cannot provide stable and reliable positioning results. Usually, additional sensors are needed to assist GNSS. This paper investigates the GNSS positioning algorithm assisted by pedestrian dead reckoning (PDR) in complex scenarios. First, we introduce a step detection algorithm based on the peak-valley of acceleration modulus, and the Weinberg model and the Mahony algorithm in PDR are used to estimate step length and heading. On this basis, we evaluated the performance of GNSS/PDR fusion positioning in an open scenario, a semiopen scenario, and a blocked scenario, respectively. Finally, we develop a GNSS/PDR real-time positioning software, called China University of Mining and Technology-POSitioning (CUMT-POS) version 1.0, on the Android 10 platform. By comparing GNSS solutions, PDR solutions, GNSS/PDR solutions, and real-time kinematic (RTK) solutions, we verify the potential auxiliary ability of PDR for GNSS positioning in complex environments, proving that multisource sensor fusion positioning significantly improves reliability and stability. Our research can help the realization of urban informatization and smart cities.

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors.

Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market Micro-Electro-Mechanical Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors, the standalone pedestrian dead reckoning (PDR) module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the Global Navigation Satellite System (GNSS) is one of the most popular positioning systems, and smartphone users can use it to acquire absolute coordinates. However, the smartphone's ultra-low-cost GNSS module is limited by some components such as the antenna, and so it is susceptible to serious interference from the multipath effect, which is a main error source of smartphone-based GNSS positioning. In this paper, we propose a multi-phase GNSS/PDR fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference based on smartphone and reference stations, the second phase proposes a novel multipath mitigation method based on multipath partial parameters estimation (MPPE) and a Double-Difference Code-Minus-Carrier (DDCMC) filter, and the third phase is to propose the joint stride lengths and heading estimations of the two standalone modules, to reduce the long-time drift and noise. The experimental results demonstrate that the proposed multipath error estimation can effectively suppress the double-difference multipath error exceeding 4 m, and compared to other methods, our fusion method achieves a minimum error RMSE of 1.63 m in positioning accuracy, and a minimum error RMSE of 4.71 m in long-time robustness for 20 min of continuous walking.

A LiDAR SLAM-Assisted Fusion Positioning Method for USVs.

Confronted with unmanned surface vessel (USV) operations where GNSS signals are unavailable due to obscuration and other factors, a LiDAR SLAM-assisted fusion positioning method for USVs is proposed to combine GNSS/INS positioning with LiDAR-SLAM. When the USV works in wide-open water, the carrier phase differential GNSS/INS loosely coupled integration strategy is applied to fuse and calibrate the positioning data, and the positioning information of the USV is obtained through the coordinate conversion process. The system uses a dynamic switching strategy to enter to LiDAR-SLAM positioning when GNSS signals are not available, compensating the LiDAR data with precise angle information to ensure accurate and stable positioning. The experiments show that compared with the traditional Kalman filter and adaptive Kalman filter fusion algorithms, the positioning error is reduced by 55.4% and 43.5%. The velocity error is also limited by 78.2% and 57.9%. The standard deviation and the root mean square error are stable within 0.1 m, indicating that our method has better data stability, while the probability of positioning anomaly is effectively controlled.

Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model.

In GNSS-denied environments, especially when losing measurement sensor data, inertial navigation system (INS) accuracy is critical to the precise positioning of vehicles, and an accurate INS error compensation model is the most effective way to improve INS accuracy. To this end, a two-level error model is proposed, which comprehensively utilizes the mechanism error model and propagation error model. Based on this model, the INS and ultra-wideband (UWB) fusion positioning method is derived relying on the extended Kalman filter (EKF) method. To further improve accuracy, the data prefiltering algorithm of the wavelet shrinkage method based on Stein's unbiased risk estimate-Shrink (SURE-Shrink) threshold is summarized for raw inertial measurement unit (IMU) data. The experimental results show that by employing the SURE-Shrink wavelet denoising method, positioning accuracy is improved by 76.6%; by applying the two-level error model, the accuracy is further improved by 84.3%. More importantly, at the point when the vehicle motion state changes, adopting the two-level error model can provide higher computational stability and less fluctuation in trajectory curves.

A novel positioning method of anti-punching drilling robot based on the fusion of multi-IMUs and visual image.

Anti-punching drilling robot (APDR) is the key equipment for underground rock burst relief in coal mine and the accurate position and attitude determination is the basis and premise for realizing unmanned pressure relief operation. In this paper, a novel positioning method of APDR based on spatial array inertial measurement units (IMUs) and visual image is proposed. Firstly, the spatial array layout of multiple IMUs is displayed creatively, and the data fusion model and the posture calculation process of spatial array IMUs are constructed. The superiority of proposed spatial array IMUs is verified through the motion trajectory simulation analysis and mobile carrier simulation experiments. Then, an image enhancement method (SSR-AF) is designed to overcome the problems of atomization, glare, uneven illumination and noise interference in coal mine images, so as to improve the accuracy of image feature extraction and matching. Accordingly, the positioning estimation model based on continuous frame images is established. Furthermore, the fusion positioning process of APDR is presented by using the loose coupling mode based on Kalman filtering algorithm. The position and attitude monitoring experimental platform of APDR is built in a simulated coal mine tunnel. The experimental results indicate that the calculation errors of displacement and attitude based on the proposed fusion positioning method is superior to the competing methods, which meet the actual positioning requirements, and verify the feasibility and practicability of the proposed positioning method for APDR.

Fusion Based on Visible Light Positioning and Inertial Navigation Using Extended Kalman Filters.

With the rapid development of smart technology, the need for location-based services (LBS) increases every day. Since classical positioning technology such as GPS cannot satisfy the needs of indoor positioning, new indoor positioning technologies, such as Bluetooth, Wi-Fi, and Visible light communication (VLC), have already cut a figure. VLC positioning has been proposed because it has higher accuracy, costs less, and is easier to accomplish in comparison to the other indoor positioning technologies. However, the practicality of VLC positioning is limited since it is easily affected by multipath effects and the layout of LEDs. Thus, we propose a fusion positioning system based on extended Kalman filters, which can fuse the VLC position and the inertial navigation data. The accuracy of the fusion positioning system is in centimeters, which is better compared to the VLC-based positioning or inertial navigation alone. Furthermore, the fusion positioning system has high accuracy, saves energy, costs little, and is easy to install, making it a promising candidate for future indoor positioning applications.