To provide continuous and reliable real−time precise positioning services in challenging environments and poor internet conditions, the real−time precise corrections of the BeiDou global navigation satellite system (BDS−3) PPP−B2b signal are utilized to correct the satellite orbit errors and clock offsets. In addition to this, using the complementary characteristics of the inertial navigation system (INS) and the global navigation satellite system (GNSS), a PPP−B2b/INS tight integration model is established. With observation data collected in an urban environment, the results show that PPP−B2b/INS tight integration can ensure a decimeter−level positioning accuracy; the positioning accuracies of the E, N, and U components are 0.292, 0.115, and 0.155 m, respectively, which can provide a continuous and secure position during short interruptions in the GNSS. However, there is still a gap of about 1 dm compared with the three−dimensional (3D) positioning accuracy obtained from Deutsche GeoForschungsZentrum (GFZ) real−time products, and a gap of about 2 dm compared with the GFZ post−precise products. Using a tactical inertial measurement unit (IMU), the velocimetry accuracies of the tightly integrated PPP−B2b/INS in the E, N, and U components are all about 0.3 cm/s, and the attitude accuracy of yaw is about 0.1 deg, while the pitch and roll show a superior performance of less than 0.01 deg. The accuracies of the velocity and attitude mainly depend on the performance of the IMU in the tight integration mode, and there is no significant difference between using real−time products and post products. The performance of the microelectromechanical system (MEMS) IMU and tactical IMU is also compared, and the positioning, velocimetry, and attitude determinations with the MEMS IMU are significantly worsened.

Visual simultaneous localization and mapping (SLAM) systems perform poorly or even fail to work under extreme conditions such as insufficient light, smoke and fog, and infrared camera has stronger anti-interference ability in these challenging scenes. However, the high noise and poor imaging quality of infrared cam-era severely affect the performance of infrared SLAM. Considering the imaging characteristics of infrared camera and the weak-texture features of subterranean structured scenes, a point-line combined thermal-inertial SLAM system (TPL-SLAM) is proposed. To improve the computational efficiency of point-line combined SLAM, a superior ELSED algorithm is employed to extract line fea-tures. Meanwhile, a three degrees-of-freedom (DOF) line feature optical flow tracking algorithm is proposed to track line features between continuous frames. Then, the back-end module optimizes inertial measurement unit (IMU), point, and line feature factors in real time based on a sliding window and jointly performs loop detection with the point and line features on keyframes. Extensive experiments were conducted on real-world datasets to validate the effectiveness of TPL-SLAM. The results showed that TPL-SLAM outperformed the current advanced monocular visual-inertial system (VINS). Besides, parallel loop detection with point-line features can effectively reduce the risk of false loops. The computational efficiency of the proposed line feature extraction and tracking module is superior to those of PL-VINS and EPLF-VINS and can meet the requirements of real-time operation. The data and code for line feature processing is accessible at https://github.com/Fireflyatcode/TPL_SLAM.

Global navigation satellite system (GNSS) signals are easily blocked by urban canyons, tree-lined roads, and overpasses in urban environments, making it impossible to ensure continuous and reliable positioning using only GNSS, even with the widely used precise point positioning and real-time kinematic (PPP-RTK). Since the inertial navigation system (INS) and GNSS are complementary, a tightly coupled PPP-RTK/INS model is developed to improve the positioning performance in these GNSS-challenged scenarios, in which the atmospheric corrections are used to achieve a rapid ambiguity resolution and the mechanization results from INS are utilized to assist GNSS preprocessing, re-fixing, and reconvergence. The experiment was conducted using three sets of vehicle-mounted data, and the performance of low-cost receiver and microelectromechanical system (MEMS) inertial measurement unit (IMU) was compared. The result shows that the positioning accuracy of PPP-RTK/INS can reach 2 cm in the horizontal component and 5 cm in the vertical component in the open environment. In the complex urban environment, continuous and reliable positioning can be ensured during GNSS short interruption, ambiguity can be instantaneously re-fixed with the assistance of INS, and decimeter-level positioning accuracy can be achieved. As a result, the horizontal positioning errors of more than 95% of the total epochs were within 20 cm. In addition, average positioning accuracy better than 15 cm and 30 cm in the horizontal and vertical components, respectively, can be obtained using the low-cost receiver and MEMS IMU. Compared with tactical IMU, the improvements in positioning accuracy and the ambiguity fixing rate using the geodetic receiver were more significant.

Recently, thermal-inertial odometry (TIO) using in-frared cameras has garnered the attention of some scholars due to its all-weather working ability. However, infrared cameras, with high noise and poor imaging quality, seriously affecting the performance of TIO. This issue is particularly pronounced in the application of monocular infrared camera on unmanned ground vehicles (UGV). Thus, we propose a multi-information enhanced monocular TIO for UGV using point and line features based on the specific thermal imaging characteristics of infrared cameras. Firstly, a real-time TIO with point line combination is achieved using more advanced line feature extraction and tracking scheme. Secondly, a parallel front-end for ground point feature process is developed to overcome the scale degradation issue of monocular TIO using camera-ground constraints. Besides, the redundancy in ground line feature modeling was resolved based on the geometric relationship between the camera and the ground. Finally, a common back-end is developed to perform real-time optimization within a sliding window framework in-cluding IMU pre-integration factors, point feature reprojection factors, line feature reprojection factors, camera-ground con-straint factors, and ground line constraint factors. The proposed system was validated on real-world datasets and compared with the current state-of-the-art algorithm. Experimental results show that the proposed system exhibits superior performance and can achieve accurate scale estimation in monocular TIO under insuf-ficient excitation of UGV. The accuracy of MIE-TIO has been improved by 67.88%~96.73%, 64.89%~98.15%, 73.91%~98.78%, and 47.41%~95.59%, respectively, compared with VINS-Fusion, PL-VINS, EPLF-VINS, and POL-VIO. The dataset is available at https://github.com/Fireflyatcode/MIE-TIO.