高帧率二值化视觉惯性里程计，解决空间与时间漂移问题。
TCB-VIO: Tightly-Coupled Focal-Plane Binary-Enhanced Visual Inertial Odometry
- 基于多状态约束卡尔曼滤波，实现250帧/秒的紧耦合融合
- 在400Hz IMU下实现优于ROVIO/VINS-Mono/ORBSLAM3的精度
- 专为焦平面传感器处理器阵列设计，降低延迟提升实时性
当部署在下一代焦平面传感器-处理器阵列（FPSP）上时，视觉算法可直接在图像传感器上执行，每个像素都具备处理能力。这显著降低了延迟，缓解了传统视觉传感器与处理器间数据传输的瓶颈问题。通过高帧率运行，FPSPs能有效抑制由视觉姿态估计引起的空间漂移，同时匹配惯性测量的高频输出以减少时间漂移。本文提出TCB-VIO，一种基于多状态约束卡尔曼滤波（MSCKF）的6自由度紧耦合视觉惯性里程计，在250帧/秒高帧率下运行，并利用400赫兹的IMU测量数据。实验表明，TCB-VIO在性能上超越现有先进方法：ROVIO、VINS-Mono和ORB-SLAM3。
原文摘要 · Abstract (English)
Vision algorithms can be executed directly on the image sensor when implemented on the next-generation sensors known as focal-plane sensor-processor arrays (FPSP)s, where every pixel has a processor. FPSPs greatly improve latency, reducing the problems associated with the bottleneck of data transfer from a vision sensor to a processor. FPSPs accelerate vision-based algorithms such as visual-inertial odometry (VIO). However, VIO frameworks suffer from spatial drift due to the vision-based pose estimation, whilst temporal drift arises from the inertial measurements. FPSPs circumvent the spatial drift by operating at a high frame rate to match the high-frequency output of the inertial measurements. In this paper, we present TCB-VIO, a tightly-coupled 6 degrees-of-freedom VIO by a Multi-State Constraint Kalman Filter (MSCKF), operating at a high frame-rate of 250 FPS and from IMU measurements obtained at 400 Hz. TCB-VIO outperforms state-of-the-art methods: ROVIO, VINS-Mono, and ORB-SLAM3.
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