Abstract: Visual odometry endows robots with the ability of autonomous positioning and building environmental maps, and is widely used in various unmanned devices. Visual odometry involves a large amount of image processing and calculation, but most of its deployment platforms only have extremely limited computational resources, limiting its application scope. In response to the I/O bottleneck of existing low-power visual odometry, this paper proposes a high-speed low-power visual odometry, named ELPVO, based on RGB-D cameras for the STM32F7 embedded platform. ELPVO fully considers the hardware resources of the STM32F7 platform, improves the processor utilization efficiency through DMA transmission, and further enhances the processing speed without changing the algorithm accuracy. On the STM32F767 embedded platform equipped with a 216 MHz ARM Cortex-M7 processor, with the TUM RGB-D dataset as the testing benchmark, ELPVO can achieve a processing speed of 26 frames per second for images with a resolution of 320×240, with an overall run speed improved by 84% and a run power consumption maintained at 0.7 watts.

Key words: visual odometry, low power consumption, RGB-D camera