Users are more likely to experience visually induced motion sickness (VIMS) during passive motion in virtual reality (VR), particularly in passive virtual driving scenarios. To solve this challenge, we propose a Multi-sensory Simulation of Wind Sensation (MSSWS) method to alleviate VIMS symptoms by enhancing the user’s sense of embodiment (SoE). The method utilizes low-fidelity airflow simulation to align vestibular perception with visual, auditory, and tactile cues. Then, we developed an interactive wind simulation helmet that can generate low-fidelity airflow along four directional axes around the user’s head, and implemented a passive virtual motorcycle riding environment with synchronized visual and auditory feedback matching the airflow patterns. A user experiment was conducted to systematically evaluate the effectiveness of MSSWS in enhancing SoE and reducing VIMS under various conditions, including helmet activation states, speed variations, and movement directions, with additional validation conducted in a Cave Automatic Virtual Environment (CAVE). Experimental results show that MSSWS significantly enhances sense of presence and SoE while substantially reducing VIMS during passive navigation. Notably, MSSWS shows significantly greater effects on VIMS in high-risk conditions characterized by rapid speed changes and multi-directional movement.

Drones are gaining increasing popularity in bush search and rescue operations, where enhancing the accuracy of occluded target detection is crucial to finding the targets. However, airborne LiDAR and multispectral cameras are limited by cost, efficiency, and imaging quality, while RGB- or thermal-based detection methods rely heavily on model performance and prior knowledge. Recovering the target in imaging is a new frontier for occluded target perception. Here, we propose a novel Synthetic Aperture Fusion Imaging (SAFI) method for drones, combining an RGBT fusion network with synthetic aperture imaging to recover occluded targets. Firstly, we propose a Color-contrast RGBT fusion Network (C-RGBTNet) based on transformer. C-RGBTNet captures target thermal features from thermal images and assigns RGB color features to occlusions, generating high-quality fusion images. Then, we apply HSV-based color segmentation to isolate the target with strong thermal intensity (high-brightness) from the cluttered background. Finally, we perform Synthetic Aperture Imaging (SAI), synthesizing the different segmented target parts to yield an occlusion-free target image. Experimental results in our home-brew Downward-view Multimodal Bush (DMB) dataset demonstrate that SAFI achieves high-quality target imaging with effective occlusion removal, significantly outperforming traditional methods.

Recently, indoor positioning has played a critical role in many emerging indoor applications. However, due to complicated indoor environments, it is still challenging to develop an indoor positioning system with high positioning accuracy and low deployment efforts. In this work, an indoor positioning system based on visible light fingerprinting is proposed by leveraging a novel visible light GraphSLAM (VL-GraphSLAM) technique. The proposed VL-GraphSLAM provides enhanced solutions at both frontend and backend to improve the accuracy of estimated trajectories. Then, the estimated trajectory is anchored in floor map based on a novel door detection method to recover indoor walking paths. Based on VL-GraphSLAM, we construct a database with the visible light received signal strength labeled by the locations of walking paths, which is called visible light map. Moreover, a Kalman filter is adopted to fuse the visible light fingerprinting and inertial sensors to locate users. Comprehensive experiments illustrate that our proposed system can accurately recover walking paths (0.4 m) and locate users (0.9 m) in an accuracy of submeter, which significantly outperforms a traditional WiFi-based fingerprinting system and is more convenient to deploy than a traditional visible light positioning based on ranging.