获取大疆无人机的飞控记录数据并绘制曲线

机型M350RTK,其飞行记录文件为加密的,我的完善代码如下

git@github.com:huashu996/DJFlightRecordParsing2TXT.git

一、下载安装官方的DJIFlightRecord

复制代码
git clone git@github.com:dji-sdk/FlightRecordParsingLib.git

飞行记录文件在打开【我的电脑】,进入遥控器内存,

文件路径:此电脑 > pm430 > 内部共享存储空间> DJI > com.dji.industry.pilot > FlightRecord

二、注册成为大疆开发者获取SDK 密钥

网址如下DJI Developer

注册完之后新建APP获得密钥

  1. 登录 DJI 开发者平台,点击"创建应用",App Type 选择 "Open API",自行填写"App 名称""分类"和"描述",点击 "创建"。

  2. 通过个人邮箱激活 App,在开发者网平台个人点击查看对应 App 信息,其中 App Key 即为下文所需的 SDK 密钥参数。

三、编译运行

编译

复制代码
cd FlightRecordParsingLib-master/dji-flightrecord-kit/build/Ubuntu/FlightRecordStandardizationCpp
sh generate.sh

运行

复制代码
cd ~/FlightRecordParsingLib-master/dji-flightrecord-kit/build/Ubuntu/FRSample
export SDK_KEY=your_sdk_key_value
./FRSample /home/cxl/FlightRecordParsingLib-master/DJrecord/DJIFlightRecord_2023-07-18_[16-14-57].txt

此时会在终端打印出一系列无人机的状态信息,但还是不能够使用

于是我更改了main.cc文件,使它能够保存数据到txt文件

四、获取单个数据

上一步生成的txt文件由于参数众多是巨大的,这里我写了一个py文件用于提取重要的信息,例如提取经纬度和高度

python 复制代码
import json

# Read JSON fragment from txt file
with open("output.txt", "r") as file:
    json_data = json.load(file)

# Extract all "aircraftLocation" and "altitude" data
location_altitude_data = []
for frame_state in json_data["info"]["frameTimeStates"]:
    aircraft_location = frame_state["flightControllerState"]["aircraftLocation"]
    altitude = frame_state["flightControllerState"]["altitude"]
    location_altitude_data.append({"latitude": aircraft_location["latitude"], "longitude": aircraft_location["longitude"], "altitude": altitude})

# Write values to a new txt file
with open("xyz_output.txt", "w") as f:
    for data in location_altitude_data:
        f.write(f"latitude: {data['latitude']}, longitude: {data['longitude']}, altitude: {data['altitude']}\n")

print("Values extracted and written to 'output.txt' file.")

就能够生成只有这几个参数的txt文件

五、生成轨迹

将经纬度和高度生成xyz坐标和画图,暂定以前20个点的均值作为投影坐标系的原点

python 复制代码
from pyproj import Proj
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d as mplot3d
def read_coordinates_from_txt(file_path):
    coordinates = []
    with open(file_path, 'r') as file:
        for line in file:
            parts = line.strip().split(',')
            latitude = float(parts[0].split(':')[1].strip())
            longitude = float(parts[1].split(':')[1].strip())
            altitude = float(parts[2].split(':')[1].strip())
            coordinates.append((latitude, longitude, altitude))
    return coordinates

def calculate_avg_coordinates(coordinates):
    num_points = len(coordinates)
    avg_latitude = sum(coord[0] for coord in coordinates) / num_points
    avg_longitude = sum(coord[1] for coord in coordinates) / num_points
    return avg_latitude, avg_longitude


def project_coordinates_to_xyz(coordinates, avg_latitude, avg_longitude, origin_x, origin_y):
    # Define the projection coordinate system (UTM zone 10, WGS84 ellipsoid)
    p = Proj(proj='utm', zone=20, ellps='WGS84', preserve_units=False)

    # Project all points in the coordinates list to xy coordinate system
    projected_coordinates = [p(longitude, latitude) for latitude, longitude, _ in coordinates]

    # Calculate the relative xyz values for each point with respect to the origin
    relative_xyz_coordinates = []
    for (x, y), (_, _, altitude) in zip(projected_coordinates, coordinates):
        relative_x = x - origin_x
        relative_y = y - origin_y
        relative_xyz_coordinates.append((relative_x, relative_y, altitude))
    return relative_xyz_coordinates
    
def three_plot_coordinates(coordinates):
    # Separate the x, y, z coordinates for plotting
    x_values, y_values, z_values = zip(*coordinates)

    # Create a new figure for the 3D plot
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')

    # Plot points as a 3D scatter plot
    ax.scatter(x_values, y_values, z_values, c='blue', label='Points')

    # Connect points with lines
    for i in range(1, len(x_values)):
        ax.plot([x_values[i - 1], x_values[i]], [y_values[i - 1], y_values[i]], [z_values[i - 1], z_values[i]], 'k-', linewidth=0.5)

    # Add labels and title
    ax.set_xlabel('X (meters)')
    ax.set_ylabel('Y (meters)')
    ax.set_zlabel('Z (meters)')
    ax.set_title('Projected Coordinates - 3D')

    # Display the 3D plot in a separate window
    plt.show()

def two_plot_coordinates(coordinates):
    # Separate the x, y, z coordinates for plotting
    x_values, y_values, z_values = zip(*coordinates)

    # Create a new figure for the 2D plot
    fig = plt.figure()

    # Plot points
    plt.scatter(x_values, y_values, c='blue', label='Points')

    # Connect points with lines
    for i in range(1, len(x_values)):
        plt.plot([x_values[i - 1], x_values[i]], [y_values[i - 1], y_values[i]], 'k-', linewidth=0.5)

    # Add labels and title
    plt.xlabel('X (meters)')
    plt.ylabel('Y (meters)')
    plt.title('Projected Coordinates - 2D')
    plt.legend()

    # Display the 2D plot in a separate window
    plt.show()
    
if __name__ == "__main__":
    input_file_path = "xyz_output.txt"  # Replace with the actual input file path

    coordinates = read_coordinates_from_txt(input_file_path)

    # Use the first 10 points to define the projection coordinate system
    num_points_for_avg = 20
    avg_coordinates = coordinates[:num_points_for_avg]
    avg_latitude, avg_longitude = calculate_avg_coordinates(avg_coordinates)

    # Project the average latitude and longitude to xy coordinate system
    p = Proj(proj='utm', zone=20, ellps='WGS84', preserve_units=False)
    origin_x, origin_y = p(avg_longitude, avg_latitude)

    print(f"Average Latitude: {avg_latitude}, Average Longitude: {avg_longitude}")
    print(f"Projected Coordinates (x, y): {origin_x}, {origin_y}")

    # Project all points in the coordinates list to xy coordinate system
    first_coordinates = [(0, 0, 0)] * min(len(coordinates), num_points_for_avg)
    projected_coordinates2 = project_coordinates_to_xyz(coordinates[num_points_for_avg:], avg_latitude, avg_longitude, origin_x, origin_y)
    projected_coordinates = first_coordinates+projected_coordinates2
    # Save projected coordinates to a new text file
    output_file_path = "projected_coordinates.txt"
    with open(output_file_path, 'w') as output_file:
        for x, y, z in projected_coordinates:
            output_file.write(f"x: {x}, y: {y}, z: {z}\n")
    three_plot_coordinates(projected_coordinates)
    two_plot_coordinates(projected_coordinates)

生成xyz的txt文档

上述只以坐标为例子,想获取其他数据,改变参数即可。

相关推荐
nbsaas-boot1 小时前
Java 正则表达式白皮书:语法详解、工程实践与常用表达式库
开发语言·python·mysql
仗剑_走天涯1 小时前
基于pytorch.nn模块实现线性模型
人工智能·pytorch·python·深度学习
chao_7891 小时前
二分查找篇——搜索旋转排序数组【LeetCode】两次二分查找
开发语言·数据结构·python·算法·leetcode
chao_7896 小时前
二分查找篇——搜索旋转排序数组【LeetCode】一次二分查找
数据结构·python·算法·leetcode·二分查找
烛阴6 小时前
Python装饰器解除:如何让被装饰的函数重获自由?
前端·python
noravinsc6 小时前
django 一个表中包括id和parentid,如何通过parentid找到全部父爷id
python·django·sqlite
ajassi20006 小时前
开源 python 应用 开发(三)python语法介绍
linux·python·开源·自动化
沉默媛7 小时前
如何安装python以及jupyter notebook
开发语言·python·jupyter
Deng9452013148 小时前
基于Python的旅游数据可视化应用
python·numpy·pandas·旅游·数据可视化技术
2401_878624798 小时前
pytorch 自动微分
人工智能·pytorch·python·机器学习