Xianfei Pan

Dual-foot pedestrian navigation systems leveraging zero-velocity updates (ZUPTs) can effectively reduce heading drift by incorporating inter-foot ranging constraints. However, their performance is highly sensitive to ranging noise and zero-velocity detector accuracy–misdetections introduce outlier observations that degrade state estimation. Conventional robust kernel-based outlier suppression mitigates this issue but introduces strong non-convexities, making the optimization prone to convergence at local minima. To address these challenges, we propose a dual-foot Fpedestrian inertial navigation algorithm based on factor graph optimization (FGO) with graduated non-convexity (GNC). The system fuses foot-mounted IMU measurements, zero-velocity constraints, and inter-foot ranging information within a sliding-window factor graph. A GNC-based backend adaptively reweights factor contributions, suppressing the effect of outliers while progressively reducing the non-convexity introduced by robust kernels. Extensive experiments demonstrate that the proposed method enhances robustness and positioning accuracy across diverse and complex pedestrian motion conditions, outperforming conventional EKF-based and standard FGO approaches.

One of the core advantages of SE2(3) Lie group framework for navigation modeling lies in the autonomy of error propagation. In the previous paper, the theoretical analysis of autonomy property of navigation model in inertial, earth and world frames was given. A construction method for SE2(3) group navigation model is proposed to improve the non-inertial navigation model toward full autonomy. This paper serves as a counterpart to previous paper and conducts the real-world strapdown inertial navigation system (SINS)/odometer(ODO) experiments as well as Monte-Carlo simulations to demonstrate the performance of improved SE2(3) group based high-precision navigation models.

One of core advantages of the SE2(3) Lie group framework for navigation modeling lies in the autonomy of error propagation. Current research on Lie group based extended Kalman filters has demonstrated that error propagation autonomy holds in low-precision applications, such as in micro electromechanical system (MEMS) based integrated navigation without considering earth rotation and inertial device biases. However, in high-precision navigation state estimation, maintaining autonomy is extremely difficult when considering with earth rotation and inertial device biases. This paper presents the theoretical analysis on the autonomy of SE2(3) group based high-precision navigation models under inertial, earth and world frame respectively. Through theoretical analysis, we find that the limitation of the traditional, trivial SE2(3) group navigation modeling method is that the presence of Coriolis force terms introduced by velocity in non-inertial frame. Therefore, a construction method for SE2(3) group navigation models is proposed, which brings the navigation models closer to full autonomy.

Large-scale camera localization in driving environments is pivotal in autonomous driving and robotics, where deep learning-based solutions offer promise for end-to-end problem-solving. However, existing learning-based camera pose regression methods often encounter accuracy and robustness challenges in complex environments, hampering scalability to large scenarios and datasets. This work introduces EMaxLoc, a novel end-to-end camera pose regression model that integrates a Multi-axis Vision Transformer (MaxViT) with an Efficient Multi-scale Attention (EMA) module for large-scale localization. The introduced MaxViT extractor captures both local and global spatial features through its block and grid attention mechanisms, while the EMA module enhances multi-scale feature fusion and channel-wise contextual modeling. EMaxLoc demonstrates remarkable proficiency in environments characterized by scene dynamics and illumination changes. Furthermore, our Transformer-based framework showcases scalability, suggesting potential for continual improvement in localization performance. Extensive experiments validate EMaxLoc’s superior performance with increasing dataset sizes, surpassing existing deep learning-based methods such as AtLoc, PoseNet, MapNet and EffLoc in large-scale driving environments.

The incorrect estimation of the signed distance function (SDF) values can have a significant impact on the localization and mapping of implicit neural dense SLAM, which is essential for autoonomous robots. To tackle the aforementioned issue, we present UcPIN-SLAM, a LiDAR SLAM using implicit neural representation with uncertainty modeling, to address the impact of noise variability under different incident angles on SLAM for autonomous robots navigation. Firstly, the noise heteroscedasticity parameters are calculated by the incident angle utilizing the lidar measurement noise model. Secondly, the maximum likelihood estimation is achieved according to the noise heteroscedasticity parameters as well as the Laplace distribution, while utilizing a self-decoder trained online via gradients for SDF value corrections. Thirdly, a multi-layer perceptron (MLP) model with loss function,which consists of endpoint constraints(END), maximum probability density function constraints(UNC), binary cross entropy (BCE) and Eikonal regularisation(ENIKNOL), is applied to further SDF calibration. Finally, the sparse optimizable neural points are defined to initialize and real-time update the implicit neural map, constructing a globally consistent high-fidelity implicit neural grid map. Two publicly available outdoor LiDAR datasets are evaluated separately for localization and mapping performance validation. The experiments demonstrate that UcPIN-SLAM achieves positioning accuracy comparable to state-of-the-art LiDAR odometry or SLAM systems, and its mapping quality outperforms the current best LiDAR SLAM methods by up to 15.7% in key metrics.

Based on the application background of magnetic directional navigation on ground-unmanned platforms. The motion of the ground carrier is limited to the two-dimensional plane, which causes the observation matrix composed of magnetic data to be seriously ill conditioned, and it is difficult for traditional methods to estimate stable compensation parameters. In this paper, a regularization method is introduced and combined with Truncated Total Least Squares (TTLS) method and Tikhonov regularization to solve ill-conditioned problems. We control the stability of the solution by introducing regularization variables. During the test, the carrier did not need to travel a specific trajectory (such as an "8" shape) and did not rely on attitude assistance equipment. We verified this method through ground-mounted tests. Test results show that in the case of strong external magnetic interference, this method can solve the ill-conditioned problem of the magnetometer observation matrix by using relatively simple operations, and effectively improve the compensation accuracy of carrier interference magnetic field. The RMSE of the compensated magnetic heading error is better than 0.9°, which is approximately 75% higher than that of the PSO. This method lays the foundation for magnetic field assisted inertial navigation.

Inertial sensors are widely utilized in smartphones, drones, vehicles, and wearable devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors’ measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learning-based techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.

Accurately perceiving location and scene is crucial for autonomous driving and mobile robots. Recent advances in deep learning have made it possible to learn egomotion and depth from monocular images in a self-supervised manner, without requiring highly precise labels to train the networks. However, monocular vision methods suffer from a limitation known as scale-ambiguity, which restricts their application when absolute-scale is necessary. To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images. In particular, we introduce a novel coarse-to-fine training strategy that enables the metric scale to be recovered in a two-stage process. Furthermore, SelfOdom is flexible and can incorporate inertial data with images, which improves its robustness in challenging scenarios, using an attention-based fusion module. Our model excels in both normal and challenging lighting conditions, including difficult night scenes. Extensive experiments on public datasets have demonstrated that SelfOdom outperforms representative traditional and learning-based VO and VIO models.