Significance: In plants, the retromer complex (VPS35, VPS29, VPS26) has been implicated in retrograde traffic from the endosomes back to the trans -Golgi network. Nevertheless, vps35 mutants exhibit impaired anterograde traffic toward the vacuole (synthetic, endocytic, and autophagic). This paradox remains an open question. We show that the small molecule endosidin17 (ES17) targets VPS35, which disrupts its association with RABG3f. This, in turn, impairs retromer complex membrane anchoring, producing vps35 mutant-like phenotypes. Using ES17, we found that VPS35–RABG3f binding precedes the release of RABG3f–GTP to interact with the vacuolar homotypic fusion and vacuole protein sorting (HOPS) complex, which is required for fusion of late compartments with the tonoplast. Thus, VPS35–RABG3f interaction constitutes a checkpoint before proceeding with membrane fusion.

So far, the accuracy of object detection in natural images has been continuously improved, and at the same time, object detection has received more and more attention in the field of remote sensing. However, different from natural images, remote sensing images have a large number of multiscale objects and large, complex backgrounds. As the size of remote sensing images is generally large, small objects are easy to lose information after downsampling. To deal with these problems, we propose a multiscale feature adaptive fusion (MFAF) method. Specifically, we first use the multiscale feature integration (FI) module and the spatial attention weight (SAW) module to construct the feature fusion module to achieve the adaptive fusion of multiscale features. Then, we add a detail enhancement (DE) module at the back of the backbone to enhance the quality of features in each scale. Next, we obtain the relationship between the features of different channels by embedding a squeeze and excitation (SE) block to highlight the useful features. Finally, a cross stage partial (CSP) block is adopted to replace continuous convolutions to reduce the number of parameters and the loss of features. We implement our work in YOLOv4. In experiments, we evaluate our method on large-scale remote sensing image datasets HRRSD and DIOR, and, respectively, achieve improvements of 2.7% and 1.9% in AP ₅₀ compared with YOLOv4, successfully improving the multiscale object detection performance.

When the wearer of Wearable Human-Machine Interaction System (WHMIS) is talking a strenuous walk, the Inertial Measurement Units (IMUs) installed on the extremities are likely to be beyond the range of design specification of the IMU sensors. This overrange leads to poor navigation performances of the system. To overcome the problem, this paper proposes a deep learning-based method to realize robust autonomous navigation. Specifically, the Faster Region-Convolutional Neural Network (R-CNN) is built to recognize the gait types of the wearer. For each gait type, the inertial data of the wearer's thigh and the foot are collected at the same frequency as the training samples, and then used to construct a correspondent Gated Recurrent Unit-Support Vector Regression (GRU-SVR) deep hybrid neural network model for virtual IMU simulation. Then, the virtual foot-mounted IMU will be constructed in real-time by the physical thigh-mounted IMU based on the neural network models mentioned above. Once the over-range of the physical foot-mounted IMU is detected, the virtual foot inertial information will be used to reconstruct the WHMIS navigation system. Experimental results show that the proposed navigation method has the ability of fault tolerance in complex gaits, and the 3D positioning performance of the reconstructed navigation system is approximately equivalent to that of the fault-free WHMIS navigation system.

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.

In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm’s versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving.

Aiming at the low-cost, wide-range, and accurate measurement requirement for Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) on a multi-rotor Unmanned Aerial Vehicle (UAV), the paper designs a heterogeneous parallel redundancy configuration scheme. In redundant MEMS IMUs, a high-cost and small-range MEMS gyroscope is combined with low-cost and large-range MEMS gyroscopes. Then, an adaptive data fusion method of redundant MEMS gyroscopes is proposed. By the designed experiments based on the simulation data and the sensor measurement data, the proposed method has been proved that it can effectively improve the angular rate measurement performance of the multi-rotor UAV and broaden the angular rate measurement range on the basis of saving the configuration cost and volume of the micro IMU.