基于焦平面二值特征的视觉惯性里程计（BIT-VIO）
Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO)
AI总结：
针对移动机器人对高帧率低功耗视觉里程计的需求，提出首个基于SCAMP-5焦平面传感器的BIT-VIO，通过松耦合iEKF融合300FPS视觉里程计与400Hz IMU数据，实现准确平滑的轨迹估计。
摘要：
焦平面传感器处理器阵列（FPSP）是一种可直接在图像传感器上执行视觉算法的新兴技术。与传统相机不同，FPSP在图像平面的单个像素上执行计算，可实现高帧率图像处理且功耗低，非常适合移动机器人。SCAMP-5等FPSP采用并行处理，基于单指令多数据流（SIMD）范式。本文提出BIT-VIO，这是首个利用SCAMP-5的视觉惯性里程计（VIO）。BIT-VIO是一种松耦合迭代扩展卡尔曼滤波器（iEKF），它将以300 FPS高速运行的视觉里程计与400 Hz IMU测量的预测结果相融合，可提供准确平滑的轨迹。
英文摘要：
Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. Unlike conventional cameras, FPSPs perform computation on the image plane -- at individual pixels -- enabling high frame rate image processing while consuming low power, making them ideal for mobile robotics. FPSPs, such as the SCAMP-5, use parallel processing and are based on the Single Instruction Multiple Data (SIMD) paradigm. In this paper, we present BIT-VIO, the first Visual Inertial Odometry (VIO) which utilises SCAMP-5.BIT-VIO is a loosely-coupled iterated Extended Kalman Filter (iEKF) which fuses together the visual odometry running fast at 300 FPS with predictions from 400 Hz IMU measurements to provide accurate and smooth trajectories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
