Direct Near-Infrared-Depth Visual SLAM With Active Lighting
Da Kong, Yu Zhang, Weichen Dai
Abstract
Since visible cameras rely on ideal illumination to provide adequate environment information, visual simultaneous localization and mapping (SLAM) under extreme illumination remains a challenge. Therefore, we propose a direct near-infrared-depth visual SLAM method with an active near-infrared (NIR) light source. The NIR light source provides necessary lighting conditions without interference to human activities. The proposed method utilizes a direct bundle adjustment method jointly fusing near-infrared and depth images to optimize camera motion. In the front-end tracking, the albedo transformed images and depth maps are used in a direct image alignment. During the back-end optimization, the direct bundle adjustment method exploits the albedo-consistency clues to minimize the geometry and albedo residuals. The indoor experiments show that the proposed method can provide robust performances without being affected by visible illumination. Compared with leading SLAM methods using active light source, the proposed method still offers competitive and robust results. The results also show that NIR-based direct SLAM outperforms the same method based on visible light, indicating that the NIR-based implementation is a potential solution for robust pose estimation indoors.