ROS惯导数据发布（Python）

时间：2018-11-06 11:08:09 阅读：347 评论：0 收藏：0 [点我收藏+]

一、背景

　　基本配置：ubuntu 16.04，ROS Kinetic

　　惯导型号：维特智能 WT61C（六轴惯导）

　　维特智能官方提供的参考程序是通过手动比较各个字节来确定数据包/数据帧的，个人认为比较繁琐，因此采用Python的re（正则表达式）和struct（字节处理）模块简化其数据匹配和提取，并实现惯导数据在ROS中的发布。

二、程序

　　惯导程序如下：

#!/usr/bin/env python


import math
import re
import serial
import struct
import time

import rospy
from geometry_msgs.msg import Quaternion, Vector3
from sensor_msgs.msg import Imu


cov_orientation = [0.00174533, 0, 0, 0, 0.00174533, 0, 0, 0, 0.00174533]
cov_angular_velocity, cov_linear_acceleration = [0.00104720, 0, 0, 0, 0.00104720, 0, 0, 0, 0.00104720], [0.0049, 0, 0, 0, 0.0049, 0, 0, 0, 0.0049]


def eul_to_qua(Eular):
    Eular_Div = [0, 0, 0]
    Eular_Div[0], Eular_Div[1], Eular_Div[2] = Eular[0]/2.0, Eular[1]/2.0, Eular[2]/2.0
    
    ca, cb, cc = math.cos(Eular_Div[0]), math.cos(Eular_Div[1]), math.cos(Eular_Div[2])
    sa, sb, sc = math.sin(Eular_Div[0]), math.sin(Eular_Div[1]), math.sin(Eular_Div[2])
    
    x = sa*cb*cc - ca*sb*sc
    y = ca*sb*cc + sa*cb*sc
    z = ca*cb*sc - sa*sb*cc
    w = ca*cb*cc + sa*sb*sc

    orientation = Quaternion()
    orientation.x, orientation.y, orientation.z, orientation.w = x, y, z, w
    return orientation


accCali = "\xff\xaa\x67"
feature = "UQ(.{6,6}).{3,3}UR(.{6,6}).{3,3}US(.{6,6}).{3,3}"
fmt_B, fmt_h = "BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB", "<hhh"


if __name__ == "__main__":
    rospy.init_node("imu")

    port = rospy.get_param("~port", "/dev/imu")
    imuHandle = serial.Serial(port=port, baudrate=115200, timeout=0.5)
    
    if imuHandle.isOpen():
        rospy.loginfo("SUCCESS")
    else:
        rospy.loginfo("FAILURE")

    # Accelerometer Calibration
    time.sleep(0.5)
    imuHandle.write(accCali) 
    time.sleep(0.5)

    imuPub = rospy.Publisher("imu", Imu, queue_size=10)

    while True:
        data = imuHandle.read(size=65)
        stamp = rospy.get_rostime()
        result = re.search(feature, data)

        if result:
            # check and get data
            frame = struct.unpack(fmt_B, result.group())
            sum_Q, sum_R, sum_S = 0, 0, 0
            for i in range(0, 10):
                sum_Q, sum_R, sum_S = sum_Q+frame[i], sum_R+frame[i+11], sum_S+frame[i+22]
            sum_Q, sum_R, sum_S = sum_Q&0x000000ff, sum_R&0x000000ff, sum_S&0x000000ff

            if (sum_Q==frame[10]) and (sum_R==frame[21]) and (sum_S==frame[32]):
                af, wf, ef = struct.unpack(fmt_h, result.group(1)), struct.unpack(fmt_h, result.group(2)), struct.unpack(fmt_h, result.group(3))

                af_l, wf_l, ef_l = [], [], []
                for i in range(0, 3):
                    af_l.append(af[i]/32768.0*16*-9.8), wf_l.append(wf[i]/32768.0*2000*math.pi/180), ef_l.append(ef[i]/32768.0*math.pi)

                imuMsg = Imu()
                imuMsg.header.stamp, imuMsg.header.frame_id = stamp, "base_link"
                imuMsg.orientation = eul_to_qua(ef_l)
                imuMsg.orientation_covariance = cov_orientation
                imuMsg.angular_velocity.x, imuMsg.angular_velocity.y, imuMsg.angular_velocity.z = wf_l[0], wf_l[1], wf_l[2]
                imuMsg.angular_velocity_covariance = cov_angular_velocity
                imuMsg.linear_acceleration.x, imuMsg.linear_acceleration.y, imuMsg.linear_acceleration.z = af_l[0], af_l[1], af_l[2]
                imuMsg.linear_acceleration_covariance = cov_linear_acceleration

                imuPub.publish(imuMsg)

        time.sleep(0.01)