Efficient and Accurate Downfacing Visual–Inertial Odometry

Visual-inertial odometry (VIO) is a widely used computer vision method that determines an agent's movement through a camera and an IMU sensor. This article presents an efficient and accurate VIO pipeline optimized for applications on micro- and nano-UAVs. The proposed design incorporates state-of-the-art feature detection and tracking methods [SuperPoint (SP), PX4FLOW, and oriented FAST and rotated BRIEF (ORB)], all optimized and quantized for emerging RISC-V-based ultralow-power parallel systems on chips (SoCs). Furthermore, by employing a rigid-body motion model, the pipeline reduces estimation errors and achieves improved accuracy in planar motion scenarios. The pipeline's suitability for real-time VIO is assessed on an ultralow-power SoC in terms of compute requirements and tracking accuracy after quantization. The pipeline, including the three feature-tracking methods, was implemented on the SoC for real-world validation. This design bridges the gap between high-accuracy VIO pipelines that are traditionally run on computationally powerful systems and lightweight implementations suitable for microcontrollers. The optimized pipeline on the GAP9 low-power SoC demonstrates an average reduction in RMSE of up to a factor of 3.65× over the baseline pipeline when using the ORB feature tracker. The analysis of the computational complexity of the feature trackers further shows that PX4FLOW achieves on-par tracking accuracy with ORB at a lower runtime for movement speeds below 24 pixels/frame.