Achieving continuous and high-precision positioning services via smartphone under a Global Navigation Satellite System (GNSS)-degraded environment is urgently demanded by the mass market. In 2018, Xiaomi launched the world’s first dual-frequency GNSS smartphone, Xiaomi Mi 8. The newly added L5/E5 signals are more precise and less prone to distortions from multipath reflections. This paper discusses the characteristics of raw dual-frequency GNSS observations from Xiaomi Mi 8 in urban environments; they are characterized by high pseudorange noise and frequent signal interruption. The traditional dual-frequency ionosphere-free combination is not suitable for Xiaomi Mi 8 raw GNSS data processing, since the noise of the combined measurements is much larger than the influence of the ionospheric delay. Therefore, in order to reasonably utilize the high precision carrier phase observations, a time differenced positioning filter is presented in this paper to deliver continuous and smooth navigation results in urban environments. The filter first estimates the inter-epoch position variation (IEPV) with time differenced uncombined L1/E1 and L5/E5 carrier phase observations and constructs the state equation with IEPV to accurately describe the user’s movement. Secondly, the observation equations are formed with uncombined L1/E1 and L5/E5 pseudorange observations. Then, kinematic experiments in open-sky and GNSS-degraded environments are carried out, and the proposed filter is assessed in terms of the positioning accuracy and solution availability. The result in an open-sky environment shows that, assisted with L5/E5 observations, the root mean square (RMS) of the stand-alone horizontal and vertical positioning errors are about 1.22 m and 1.94 m, respectively, with a 97.8% navigation availability. Encouragingly, even in a GNSS-degraded environment, smooth navigation services with accuracies of 1.61 m and 2.16 m in the horizontal and vertical directions are obtained by using multi-GNSS and L5/E5 observations.

Accurate vehicle ego-localization is key for autonomous vehicles to complete high-level navigation tasks. The state-of-the-art localization methods adopt visual and light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) to estimate the position of the vehicle. However, both of them may suffer from error accumulation due to long-term running without loop optimization or prior constraints. Actually, the vehicle cannot always return to the revisited location, which will cause errors to accumulate in Global Navigation Satellite System (GNSS)-challenged environments. To solve this problem, we proposed a novel localization method with prior dense visual point cloud map constraints generated by a stereo camera. Firstly, the semi-global-block-matching (SGBM) algorithm is adopted to estimate the visual point cloud of each frame and stereo visual odometry is used to provide the initial position for the current visual point cloud. Secondly, multiple filtering and adaptive prior map segmentation are performed on the prior dense visual point cloud map for fast matching and localization. Then, the current visual point cloud is matched with the candidate sub-map by normal distribution transformation (NDT). Finally, the matching result is used to update pose prediction based on the last frame for accurate localization. Comprehensive experiments were undertaken to validate the proposed method, showing that the root mean square errors (RMSEs) of translation and rotation are less than 5.59 m and 0.08°, respectively.

Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to degeneration, which leads to the failure of mobile robot localization and mapping. To address these issues, this paper proposes a robust LiDAR SLAM method which detects and compensates for the degenerated scenes by integrating LiDAR and inertial measurement unit (IMU) data. First, the disturbance model is used to detect the direction and degree of degeneration caused by insufficient line and plane feature constraints for obtaining the factor and vector of degeneration. Second, the degenerated state is divided into rotation and translation. The pose obtained by IMU pre-integration is projected to plane features and then used for local map matching to achieve two-step degenerated compensation. Finally, a globally consistent LiDAR SLAM is implemented based on sliding window factor graph optimization. The extensive experimental results show that the proposed method achieves better robustness than LeGO-LOAM and LIO-SAM. The absolute position root mean square error (RMSE) is only 0.161 m, which provides an important reference for underground autonomous localization and navigation in intelligent mining and safety inspection.

In response to rapid change of attitude in complex situations such as sharp turns and camera jitter, visual odometry/visual simultaneous localization and mapping (VO/VSLAM) often requires a high camera frame rate. However, the high frame rate of the camera poses a great challenge to real-time VO/VSLAM, especially when the vehicle travels along a relatively flat trajectory which requires higher real-time performance. Therefore, in this paper we propose an automatic method for key-frame selection based on the motion state of the vehicle, which can reduce data redundancy and improve the real-time performance and robustness of VO/VSLAM. Firstly, pyramid layered Kanade-Lucas-Tomasi (KLT) algorithm is used to track the feature points, five-point method and RANSAC are used to calculate the essential matrix. Secondly, two-step decomposition is used to calculate the change of inter-frame attitude, and then key-frames are automatically selected based on the motion state of the vehicle predicted by attitude change within a short time interval. To evaluate the method, we conduct extensive experiments on real data and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset based on monocular visual odometry. Then we made qualitative and quantitative evaluations from the comparison with the reference trajectory, relative error (RE), root mean square error (RMSE), and absolute trajectory error (ATE). Results indicate that our method can improve the real-time performance while ensuring accuracy, with the data redundancy reduced by about 40-60%. In addition, the performance of our method is further verified by comparison with the current representative ORB-SLAM.

Digital reconstruction of ancient buildings is very challenging due to its architectural complexity and structural delicacy. Therefore, how to apply new Earth Observation (EO) technology to digitally reconstruct the complete and real model of ancient buildings without damage has become an urgent issue. This article proposes a multi-scan point cloud hierarchical registration method for 3D reconstruction of the ancient Chinese Great Wild Goose Pagoda based on Terrestrial Laser Scanner (TLS) data. First, a multi-scan point cloud hierarchical registration method is elaborately designed for 3D reconstruction. Then, the outline feature of the ancient Chinese Great Wild Goose Pagoda is automatically extracted by point cloud slicing. Finally, fine modeling is carried out according to the outline feature extracted from the reconstructed point cloud, and after that block combination and texture mapping are conducted for 3D digitalization and documentation. The experiments indicate that the Root Mean Square Error (RMSE) of multi-scan hierarchical registration of the translation and rotation are less than 3 cm and 0.862 ^∘ , respectively, and the complete and real model of the Great Wild Goose Pagoda is obtained by fine modeling and texture mapping, which provides an important reference for 3D digital protection of ancient buildings.