We propose a novel information gain metric that combines hand-crafted and data-driven metrics to address the next best view problem for autonomous 3D mapping of unknown indoor environments. For the hand-crafted metric, we propose an entropy-based information gain that accounts for the previous view points to avoid the camera to revisit the same location and to promote the motion toward unexplored or occluded areas. Whereas for the learnt metric, we adopt a Convolutional Neural Network (CNN) architecture and formulate the problem as a classification problem. The CNN takes as input the current depth image and outputs the motion direction that suggests the largest unexplored surface. We train and test the CNN using a new synthetic dataset based on the SUNCG dataset. The learnt motion direction is then combined with the proposed hand-crafted metric to help handle situations where using only the hand-crafted metric tends to face ambiguities. We finally evaluate the autonomous paths over several real and synthetic indoor scenes including complex industrial and domestic settings and prove that our combined metric is able to further improve the exploration coverage compared to using only the proposed hand-crafted metric.

Navigational aids play a vital role in enhancing the mobility and independence of blind and visually impaired (VI) individuals. However, existing solutions often present challenges related to discomfort, complexity, and limited ability to provide detailed environmental awareness. To address these limitations, we introduce SnapStick, an innovative assistive technology designed to improve spatial perception and navigation. SnapStick integrates a Bluetooth-enabled smart cane, bone-conduction headphones, and a smartphone application powered by the Florence-2 Vision Language Model (VLM) to deliver real-time object recognition, text reading, bus route detection, and detailed scene descriptions. To assess the system’s effectiveness and user experience, eleven blind participants evaluated SnapStick, and usability was measured using the System Usability Scale (SUS). In addition to the 94% accuracy, the device received an SUS score of 84.7%, indicating high user satisfaction, ease of use, and comfort. Participants reported that SnapStick significantly improved their ability to navigate, recognize objects, identify text, and detect landmarks with greater confidence. The system’s ability to provide accurate and accessible auditory feedback proved essential for real-world applications, making it a practical and user-friendly solution. These findings highlight SnapStick’s potential to serve as an effective assistive device for blind individuals, enhancing autonomy, safety, and navigation capabilities in daily life. Future work will explore further refinements to optimize user experience and adaptability across different environments.