Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

Simultaneous Localization and Mapping (SLAM) in an unknown environment is a crucial part for intelligent mobile robots to achieve high-level navigation and interaction tasks. As one of the typical LiDAR-based SLAM algorithms, the Lidar Odometry and Mapping in Real-time (LOAM) algorithm has shown impressive results. However, LOAM only uses low-level geometric features without considering semantic information. Moreover, the lack of a dynamic object removal strategy limits the algorithm to obtain higher accuracy. To this end, this paper extends the LOAM pipeline by integrating semantic information into the original framework. Specifically, we first propose a two-step dynamic objects filtering strategy. Point-wise semantic labels are then used to improve feature extraction and searching for corresponding points. We evaluate the performance of the proposed method in many challenging scenarios, including highway, country and urban from the KITTI dataset. The results demonstrate that the proposed SLAM system outperforms the state-of-the-art SLAM methods in terms of accuracy and robustness.

In this paper, we propose and experimentally demonstrate an interferometric fiber optic gyroscope (IFOG) composed of an optimized elliptical-core Panda polarization-maintaining fiber (PMF) based on a comprehensive analysis of the design principle of PMFs for IFOGs. The improvement mechanism of the geometric birefringence on the performance of the Panda PMF was analyzed using the finite element model, and the optimized structural parameters were selected for fiber fabrication. The extinction ratio, polarization crosstalk, and assembled closed-loop IFOG output were tested and compared for fiber coils wound by different fiber structures under different temperature loads. An angle random walk performance of 0.001723 °/√h was achieved, which is a significant improvement over the traditional IFOG. The experimental results confirmed the proposed design basis that the elliptical-core Panda PMF with both higher birefringence and lower effective mode refractive index can improve the temperature stability of the IFOG.

This article focuses on addressing the non-linear state estimations problem of measurement noise with non-Gaussian distribution, which is critical for the performance of the inertial navigation system (INS)/odometer (OD) integrated navigation system. Although many existing robust filters have been proposed to address the non-Gaussian issue, their performance is not quite ideal for non-Gaussian noise in real applications, such as impulsive and noises from multimodal distributions. Meanwhile, the computational complexity burden impedes the application of robust non-linear algorithms in INS/OD integrated navigation systems. This paper proposes a minimal Cauchy kernel Cubature Kalman filter (MCK-CKF) to address the non-Gaussian problem in general non-linear systems. In addition, a simplified MCK-CKF (SMCK-CKF) is proposed to reduce the computational cost in INS/OD integrated navigation system, meanwhile ensuring outstanding performance similar to MCK-CKF. First, we derive the MCK-CKF based on the new cost function, which is obtained by a combination of weighted least square (WLS) and MCK. Then, for the INS/OD integrated navigation non-linear system, which has a linear measurement equation, the SMCK-CKF is established by introducing the approximate method to calculate the matrix inversion in the combination of Kalman filter measurement updated and MCK-CKF framework. The computational complexity of the CKF/MCK-CKF/SMCK-CKF is analyzed and compared. The powerful robustness for tackling various forms of non-Gaussian interference of MCK-CKF is validated by simulation on a typical non-linearity system. Furthermore, the vehicle’s INS/OD integrated navigation experiment demonstrates that the MCK-CKF/SMCK-CKF have remarkable robustness, and the SMCK-CKF has outstanding time efficiency.

Research on the performance of polarization-maintaining fiber (PMF) for fiber coils is significant for the precision improvement of an interferometric fiber optic gyroscope (IFOG) working in harsh environments. In this paper, we firstly report analytical models of the fiber polarization theory and present two types of hybrid PMF structures by a collaboration of geometry and stress effects: a Panda-type horizontal-elliptical core PMF (Panda-type HE-PMF) based on a superposition of geometry and stress, and a Panda-type longitudinal-elliptical core PMF (Panda-type LE-PMF) with geometry offset stress effect, achieving enhanced and suppressed modal performance by adjusting geometric anisotropy of elliptical-core in different directions. Then, the influence mechanisms of the geometric birefringence on the modal performance of both PMFs as the variation of fiber structural parameters are investigated via numerical simulations to determine the target fiber designs. The other significant attribute, including effective mode area (A_eff), nonlinear coefficient (γ), and chromatic dispersion (D), and their tolerance to PMF parameter fluctuations are also evaluated. Finally, both target PMFs with structural optimization are practically fabricated and wound into four fiber coils with quadrupolar (QAD) and 16-polar symmetrical winding patterns, respectively. The polarization ability and thermal performance are further demonstrated by experiments conducted on both PMFs, wound fiber coils, and built IFOGs under static and dynamic environments (over a wide temperature range of -40 °C to 70 °C), and compared with a conventional PMF. The testing results suggest that designed HE-PMF coils both achieve high birefringence, static extinction ratio (ER) values of up to 30.80 dB and 31.93 dB, respectively, corresponding to an almost one-fold increase over conventional coils. Remarkably, the ER property of the HE-PMF coil by combining this HE-PMF design and a 16-polar winding pattern consistently remains above 29.5 dB with a minimal fluctuation in ER of only 3.0 dB across the entire variable temperature conditions. The bias stability of the IFOG assembled with this coil is strongly enhanced to 0.0019 °/h and 0.082 °/h under static and dynamic conditions, respectively, which is a significant improvement over conventional coils of 0.136 °/h. Also, the static angle random walk performance of the improved IFOG is reduced to 0.000624 °/√h. In contrast, the LE-PMF as a comparison is shown to limited polarization characteristics with a low birefringence and ER due to the suppression effect of the geometric birefringence, and the applied IFOG output also exhibits larger drift, indicating a poor thermal ability. Experimental results show great agreement with theoretical analysis and numerical simulations, confirming the validity of design principles. The advances in both designs are instructive for the engineering applications of PMFs for IFOGs and for improving the accuracy of fiber sensors.

The thermal performance under variable temperature conditions of fiber coils with double-cylinder (D-CYL) and quadrupolar (QAD) winding methods is comparatively analyzed. Simulation by the finite element method (FEM) is done to calculate the temperature distribution and the thermal-induced phase shift errors in the fiber coils. Simulation results reveal that D-CYL fiber coil itself has fragile performance when it experiences an axially asymmetrical temperature gradient. However, the axial fragility performance could be improved when the D-CYL coil meshes with a heat-off spool. Through further simulations we find that once the D-CYL coil is provided with an axially symmetrical temperature environment, the thermal performance of fiber coils with the D-CYL winding method is better than that with the QAD winding method under the same variable temperature conditions. This valuable discovery is verified by two experiments. The D-CYL winding method is thus promising to overcome the temperature fragility of interferometric fiber optic gyroscopes (IFOGs).