Visual simultaneous localization and mapping (SLAM) is an emerging technology that enables low-power devices with a single camera to perform robotic navigation. However, most visual SLAM algorithms are tuned for image...
详细信息
ISBN:
(纸本)9781728163956
Visual simultaneous localization and mapping (SLAM) is an emerging technology that enables low-power devices with a single camera to perform robotic navigation. However, most visual SLAM algorithms are tuned for images produced through the imagesensor processing (ISP) pipeline optimized for highly aesthetic photography. In this paper, we investigate the feasibility of varying sensorquantization on RAW images directly from the sensor to save energy for visual SLAM. In particular, we compare linear and logarithmic imagequantization and show visual SLAM is robust to the latter. Further, we introduce a new gradient-based imagequantization scheme that outperforms logarithmic quantization's energy savings while preserving accuracy for feature-based visual SLAM algorithms. This work opens a new direction in energy-efficient image sensing for SLAM in the future.
暂无评论