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基于滤波器的松耦合 SSF 单IMU数据融合

Home Page: http://www.liuxiao.org/2016/07/ros-%e5%a4%9a%e4%bc%a0%e6%84%9f%e5%99%a8%e5%8d%a1%e5%b0%94%e6%9b%bc%e8%9e%8d%e5%90%88%e6%a1%86%e6%9e%b6-ethzasl-msf-framework-%e7%bc%96%e8%af%91%e4%b8%8e%e4%bd%bf%e7%94%a8/

CMake 3.42% Python 3.74% C++ 91.00% Shell 1.84%

ethzasl_sensor_fusion's Introduction

ethzasl_sensor_fusion

time delay compensated single and multi sensor fusion framework based on an EKF

红色字体部分是从传感器获取的数据，用于输入到预测(prediction)和更新阶段(update).
蓝色字体是更新阶段会变化的部分。
黑色部分为约束部分，是不变的。

变量：
p for pwi: 在世界坐标下的 IMU位置 IMU position in the world frame
v for vwi: 在世界坐标下的 IMU速度 IMU velocity in the world frame
q for qwi: 在世界坐标下的 IMU姿态 IMU attitude in the world frame

b_w for bw: 陀螺仪漂移 the gyro biases
b_a for ba: 陀螺仪漂移 the accelerometer biases

L for λ:    视觉尺度因子 the visual scale factor with pmetric*λ = psensor

q_wv for q,,vw: 更新阶段参考帧(相机参考帧) 和 世界参考帧 之间的姿态变化
q_ci for qic:   IMU and the update-sensor(相机) 姿态变化
p_ci for pic:   IMU and the update-sensor(相机) 位置变换

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