Weak texture and motion blur are always challenging problems for visual-inertial odometry (VIO) systems. To improve accuracy of VIO systems in the challenging scenes, we propose a point-line-based VIO system with novel feature hybrids and with novel predicting-matching for long line track. Point-line features with shorter tracks are categorized into “MSCKF” features and with longer tracks into “SLAM” features. Especially, “SLAM” lines are added into the state vector to improve accuracy of the proposed system. Besides, to ensure the reliability and stability of detection and tracking of line features, we also propose a new “Predicting-Matching” line segment tracking method to increase the track lengths of line segments. Experimental results show that the proposed method outperforms the state-of-the-art methods of VINS-Mono [1], PL-VINS [2] and OpenVINS [3]) on both a public dataset and a collected dataset in terms of accuracy. The collected dataset is full of extremely weak textures and motion blurs. On this dataset, the proposed method also obtains better accuracy than ORB-SLAM3 [4].

UAV-based object detection has recently attracted a lot of attention due to its diverse applications. Most of the existing convolution neural network based object detection models can perform well in common object detection cases. However, due to the fact that objects in UAV images are spatially distributed in a very dense manner, these methods have limited performance for UAV-based object detection. In this paper, we propose a novel transformer-based object detection model to improve the accuracy of object detection in UAV images. To detect dense objects competently, an advanced foreground enhancement attention Swin Transformer (FEA-Swin) framework is designed by integrating context information into the original backbone of a Swin Transformer. Moreover, to avoid the loss of information of small objects, an improved weighted bidirectional feature pyramid network (BiFPN) is presented by designing the skip connection operation. The proposed method aggregates feature maps from four stages and keeps abundant information of small objects. Specifically, to balance the detection accuracy and efficiency, we introduce an efficient neck of the BiFPN network by removing a redundant network layer. Experimental results on both public datasets and a self-made dataset demonstrate the performance of our method compared to the state-of-the-art methods in terms of detection accuracy.

Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training to improve their generality. To improve the reliability of visual navigation, in this paper, we propose a novel object-level topological visual navigation method. Firstly, a lightweight object-level topological semantic map is constructed to release the dependence on the precise metric map, where the semantic associations between objects are stored via graph memory and topological organization is performed. Then, we propose an object-based heuristic graph search method to select the global topological path with the optimal and shortest characteristics. Furthermore, to reduce the global cumulative error, a global path segmentation strategy is proposed to divide the global topological path on the basis of active visual perception and object guidance. Finally, to achieve adaptive smooth trajectory generation, a Bernstein polynomial-based smooth trajectory refinement method is proposed by transforming trajectory generation into a nonlinear planning problem, achieving smooth multi-segment continuous navigation. Experimental results demonstrate the feasibility and efficiency of our method on both simulation and real-world scenarios. The proposed method also obtains better navigation success rate (SR) and success weighted by inverse path length (SPL) than the state-of-the-art methods.

The genus Macrispa Baly, 1859, previously known only from Southeast Asia and south of the Himalayas, has remained unrecorded in China until now. Here, we report the first discovery of Macrispa saundersii Baly, 1859 from Xizang Autonomous Region, China, representing a northward range extension for the genus. Through integrative examination of external morphology and male genitalia, we provide a comprehensive redescription of M. saundersii. High-resolution stacked imaging was employed to document key taxonomic characters, including elytral punctation patterns, stridulatory files, abdominal ventrite structures, and male genitalia.

Cyborg insects offer a biologically powered solution for locomotion control, but conventional methods typically rely on invasive electrical stimulation. Here, we introduce a noninvasive, phototaxis-based strategy to steer walking Endebius florensis beetles using light-emitting diode (LED) stimuli. Electroretinogram recordings revealed spectral sensitivity to blue, green, and yellow light, with reduced response to red. Behavioral assays demonstrated robust positive phototaxis to blue light and negative phototaxis to yellow. Using these findings, we built a wireless microcontroller-based backpack emitting directional blue light to induce steering. The beetles reliably turned toward the activated light, achieving angular deflections over 60° within seconds. This approach enables repeatable, trauma-free insect control and establishes a new paradigm for biohybrid locomotion systems.

Vision-based multi-robot global localization in large-scale environments is a challenging task due to real-time environmental changes, which complicate data association between viewpoints. Recently, researchers have proposed methods to enhance viewpoint invariance by organizing object semantic information using graph structures. However, previous works still face limitations in accuracy and robustness in real-world scenarios, primarily due to the limited descriptive power of feature information (semantic or geometric) and susceptibility to noise. In this paper, we propose a novel semantic-geometric triple constraint-based graph matching multi-robot global localization method, called SGT-MGL. We first extract the semantic and geometric features of objects and describe the distribution of neighboring objects using topological structures. To improve object discriminability, we construct a triangular descriptor for each object based on semantic information and relative distances. Considering the complementarity between semantic and geometric information, we introduce a 3D histogram descriptor that encodes semantic, spatial angular, and relative distance information, enhancing the invariance of the triangular descriptor. To further mitigate noise, we propose a candidate point selection strategy guided by global geometric structures and employ a combined local and global graph matching approach for 6-DOF pose estimation. We extensively evaluate SGT-MGL on three public datasets, demonstrating superior accuracy and robustness. The implementation of SGT-MGL will be available.