Nan Jia

Rotational modulation inertial navigation systems (RMINSs) provide an effective system-level approach for improving inertial navigation accuracy without external aiding. By mounting the inertial measurement unit on one or multiple rotating axes, rotational modulation transforms selected constant or slowly varying sensor errors into periodic components, thereby reducing their long-term accumulation in navigation integration. Although extensive studies have investigated specific rotation schemes, calibration methods, and application-oriented implementations, the intrinsic relationship among rotational architecture, error modulation principle, and strategy design has not been sufficiently clarified from a unified system perspective. In fact, the achievable performance of an RMINS is jointly determined by these tightly coupled aspects: architecture defines the available modulation geometry and excitation capability, principle explains how inertial and structural errors evolve under rotation, and strategy determines how effectively the theoretical modulation potential is translated into practical navigation performance. Motivated by this gap, this paper presents a systematic review of RMINSs from the perspectives of architectures, principles, and strategies. Representative single-, dual-, and tri-axis RMINSs are first compared based on structural characteristics, modulation capability, engineering complexity, and typical applicability. The fundamental principles of rotational modulation are then summarized, with emphasis on the propagation and suppression of constant bias, scale factor error, misalignment error, nonorthogonality, and Earth-rate-coupled error terms. Typical rotation strategies are further critically reviewed with respect to modulation effectiveness, engineering burden, and suitability under different sensor grades and dominant error conditions. A representative-study comparison matrix and design-oriented conclusions are further provided to clarify when single-, dual-, and tri-axis architectures should be selected, and which error sources can be mainly handled by rotational modulation or must rely on calibration and compensation. Finally, current challenges and future research directions are discussed. This review aims to provide a structured reference for the analysis, design, evaluation, and practical development of high-performance RMINSs.

This paper introduces an algorithm for identifying the external parameters between a solid-state Light Detection and Ranging (LiDAR) system and a single camera. The algorithm leverages asymmetric three-dimensional targets to establish saturated constraints and uniquely determines the rotational extrinsics through dual vectors, ensuring that the resulting rotation matrix is orthogonal. Using single-frame images and point cloud data, we derive a closed-form solution for computing the external parameters and employ a global optimizer to enhance their accuracy. In the experimental section, we analyze the impact of various geometric constraints on the observability of external parameters, evaluate the algorithm’s precision, and assess its deployment efficiency on a distributed ARM cluster. Despite the algorithm’s requirement for costly stereo calibration references, it eliminates the need for frequent target or sensor movements, making it particularly suitable for calibrating large-scale equipment.

This paper proposes a novel robust orientation estimator to enhance the accuracy and robustness of orientation estimation for inertial–magnetic sensors of the small consumer–grade drones. The proposed estimator utilizes a trust–region strategy within a nonlinear optimization framework, transforming the orientation fusion problem into a nonlinear optimization problem based on the maximum likelihood principle. The proposed estimator employs a trust–region Dogleg gradient descent strategy to optimize orientation precision and incorporates a Huber robust kernel to minimize interference caused by acceleration during the maneuvering process of the drone. In addition, a novel method for evaluating the performance of orientation estimators is also presented based on visuals. The proposed method consists of two parts: offline calibration of the basic cube using Augmented Reality University of Cordoba (ArUco) markers and online orientation measurement of the sensor carrier using a nonlinear optimization solver. The proposed measurement method’s accuracy and the proposed estimator’s performance are evaluated under low–dynamic (rotation) and high–dynamic (shake) conditions in the experiment. The experimental findings indicate that the proposed measurement method obtains an average re–projection error of less than 0.1 pixels. The proposed estimator has the lowest average orientation error compared to conventional orientation estimation algorithms. Despite the time–consuming nature of the proposed estimator, it exhibits greater robustness and precision, particularly in highly dynamic environments.

The majority of object detection algorithms based on convolutional neural network are focused on larger objects. In order to improve the accuracy and efficiency of small object detection, a novel lightweight object detection algorithm with attention enhancement is proposed in this paper. The network part of the proposed algorithm is based on a single-stage framework and takes MobileNetV3-Large as a backbone. The representation of shallower scale features in the scale fusion module is enhanced by introducing an additional injection path from the backbone and a detection head specially responsible for detecting small objects is added. Instead of pooling operators, dilated convolution with hierarchical aggregation is used to reduce the effect of background pixels on the accuracy of small object locations. To improve the efficacy of merging, the spatial and channel weights of scale features are modified adaptively. Last but not least, to improve the representation of small objects in the training datasets, the Consistent Mixed Cropping method is also proposed. The small labels of standard datasets are expanded with the self-collected samples for the training of the algorithm network. According to the test results and visualization on the 64-Bit Extended (X86-64) platform and embedded Advanced RISC Machine (ARM) platform, we find that the average accuracy (mAP) of the proposed algorithm is 4.6% higher than YOLOv4 algorithm, which achieves better small object detection performance than YOLOv4 algorithm, and the computational complexity is only 12% of YOLOv4 algorithm.

In order to identify the risk of the letter and visit stability work, on the basis of summarizing the existing research methods, put forward a scheme for the monitoring and risk identification of the letter and visit information based on the big data of the Internet. Firstly, through real-time network crawler and other means to establish the labor relations field letter information monitoring data set, then based on data mining technology, relying on the network big data to carry out risk identification research on the letter and visit information, and finally based on the letter and visit information monitoring and risk identification results put forward a preventive mechanism.