PicoVO: A Lightweight RGB-D Visual Odometry Targeting Resource-Constrained IoT Devices

Abstract

Ego-motion estimation with 3D perception using visual odometry (VO) is known to be robust and economical among the existing odometry techniques. However, existing VO solutions are typically both computation intensive and memory intensive, which dramatically inhibits their deployment in IoT platforms such as robotic vehicles and handheld devices mostly equipped with resource-constrained MCU-level processors. To enable real-time and high-quality VO on these scenarios with thrifty resource budgets, we investigate state-of-the-art edge-based VO (EBVO) and propose an optimization framework called PicoVO that can greatly reduce the amount of computation as well as the memory footprint from the perspectives of both algorithm and implementation. First of all, we revisit the key processing stages of EBVO and propose an EBVO-oriented lightweight edge detector in the pre-processing stage, a sparse-to-dense processing scheme in the tracking stage, and a lightweight key-frame management in the post-processing stage. In addition to the algorithmic optimization, we further develop a dedicated quantization scheme particularly for the 3D feature calculation and Levenberg-Marquardt (LM) solver that are critical to the memory footprint and computation requirements of PicoVO. Evaluation on realistic RGB-D benchmark datasets is conducted on NUCLEO-F767ZI equipped with a 216MHz Cortex-M7 MCU and 512KB RAM. It reveals that PicoVO achieves 33fps@320x240 with high tracking precision comparable to state-of-the-art VOs on PC.