LiDAR odometry has gained popularity due to the LiDAR sensor’s accurate depth measurement and robustness to varying illumination conditions. The feature-based methods achieve the advantage of efficiency through feature extraction, while the distribution-based ones attain better accuracy by modeling the point cloud as distributions. Combining the strengths of both methods is anticipated to yield superior performance. However, existing combination schemes typically extract and process features and distributions separately, and such a loosely integrated approach cannot fully leverage their complementarity. To address this problem, we propose a novel LiDAR odometry method based on surface distributed point (SDP) feature with dual feature fusion. Specifically, the SDP feature is introduced to tightly integrate features and distributions, facilitating efficient feature association and map maintenance. On this basis, the associated source and target features are then effectively integrated through dual feature fusion to form the dual-fusion (DF) associated plane. This plane serves as the basis for constructing the point-to-DF associated plane constraint for pose optimization. As a result, the local planar structure is more accurately reflected, thereby enhancing the accuracy of pose estimation. The SDP feature and the resultant constraint are employed in both scan-to-map matching and fixed-lag smoothing, which are hierarchically organized to achieve accurate pose estimation. Experiments on KITTI dataset and large-scale KITTI-360 dataset demonstrate the effectiveness of the proposed method.

ICSI (Intracytoplasmic sperm injection) is one of the most effective treatments for severe male infertility. During the implementation of the ICSI, it is necessary to perform the three-dimensional positioning of the tip of the glass injection micropipette. At present, the process is mainly controlled by skilled operators. Such manual operation is time-consuming and likely to cause micropipette damage or even serious damage to the oocyte membrane due to the inaccurate positioning. In this paper, an automatic system was developed for micropipette positioning. The microscopic positioning is carried out by autofocusing and planar positioning. A search strategy based on Dynamic Curve Fitting (DCF) was proposed to avoid local extremum issues due to the noises during the autofocusing. The proposed search strategy is adaptive to different focus algorisms, making the design of autofocusing becomes simpler. Besides, we proposed a planar positioning algorithm that can quickly and accurately obtain the tip position of the micropipette at the focusing plane. Finally, the visual servo control is employed to move the micropipette to the center of the vision. The experimental results demonstrated that the DCF method can accurately (mean error: 3 μm) find the focusing plane when applying random Gaussian noise with a mean value of 0 and a variance of 1 to the focus measure. By contrast, both the hill-climbing search method and the Fibonacci search method cannot work properly. The tip positioning algorithm provides a real-time in-plane tip positioning at a frame rate of 40 Hz with an average accuracy of 11 μm.

Magnetic manipulation provides a versatile, remote, noninvasive, and cost-effective strategy in a variety of applications. Till now, many different configurations of magnetic manipulation systems have been developed to address different needs on force, torque, accuracy, and accessibilities. Magnetic field mapping can help to explore the exact map of the magnetic field in the working space and guarantee the homogeneity of the magnetic field. In this paper, a flexible mapping method is employed to solve the scalar potential of the magnetic source by using the separation of variables in Cartesian coordinates. Levenberg-Marquardt Algorithm (LMA) and Whale Optimization Algorithm (WOA) are set to the solver of the model. The work is evaluated in the mapping of an eight-pole magnetic manipulation system. The result of numerical simulation shows that the coefficient of determination R2of the model reaches99.81%, and the actual system mapping obtains R2value of 99.57%. This technique can directly be used to calculate the magnetic flux density and gradient field in a short period (≈1ms). Finally, the manipulation of a permanent magnet under the control magnetic field mapping and PID controller demonstrates the effectiveness of the proposed method.

Because of the randomness of biometric features, traditional methods cannot perform well in the encryption of fingerprint template. The Fuzzy Vault construct is a biometric cryptosystem which can bind the fingerprint features with the secret key, and give reliable protection to both of them. The performance of Fuzzy Vault is highly affected by the alignment of fingerprints in the encryption field. In this paper, we propose a novel helper data based on the topo-structure around the core. The topo-structure has the advantage that it does not reveal any information about the template minutiae, and can reduce the alignment calculation amount. Experimental results show that the novel helper data has a good performance in alignment between vaults with high security.