Memory-Efficient RGBD Visual Odometry for Mobile Devices

The spatial computing has been a popular topic in recent years, driving the development and use of consumer electronics such as AR/VR head-mounted displays (HMDs) and smart glasses. A critical component of these mobile devices is visual odometry (VO), which provides on-device motion tracking to allow users to interact with and move freely in virtual space. VO must be sufficiently efficient to handle real-time processing on resource-constrained mobile devices. To meet this requirement, we propose a memory-efficient algorithm from a hardware perspective, achieving over tenfold memory savings. Our architecture further reduces memory usage by 32.78%, area by 10.20%, and improves performance by 1.68x. Implemented in TSMC 40 nm technology, it demonstrates competitive results compared to other works, handling nearly three times more data due to processing the depth map.