一、下载安装官方的DJIFlightRecord

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

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"。
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

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

运行

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

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

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 coordinatesdef calculate_avg_coordinates(coordinates):num_points = len(coordinates)avg_latitude = sum(coord[0] for coord in coordinates) / num_pointsavg_longitude = sum(coord[1] for coord in coordinates) / num_pointsreturn avg_latitude, avg_longitudedef 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 systemprojected_coordinates = [p(longitude, latitude) for latitude, longitude, _ in coordinates]# Calculate the relative xyz values for each point with respect to the originrelative_xyz_coordinates = []for (x, y), (_, _, altitude) in zip(projected_coordinates, coordinates):relative_x = x - origin_xrelative_y = y - origin_yrelative_xyz_coordinates.append((relative_x, relative_y, altitude))return relative_xyz_coordinatesdef three_plot_coordinates(coordinates):# Separate the x, y, z coordinates for plottingx_values, y_values, z_values = zip(*coordinates)# Create a new figure for the 3D plotfig = plt.figure()ax = fig.add_subplot(111, projection='3d')# Plot points as a 3D scatter plotax.scatter(x_values, y_values, z_values, c='blue', label='Points')# Connect points with linesfor 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 titleax.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 windowplt.show()def two_plot_coordinates(coordinates):# Separate the x, y, z coordinates for plottingx_values, y_values, z_values = zip(*coordinates)# Create a new figure for the 2D plotfig = plt.figure()# Plot pointsplt.scatter(x_values, y_values, c='blue', label='Points')# Connect points with linesfor 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 titleplt.xlabel('X (meters)')plt.ylabel('Y (meters)')plt.title('Projected Coordinates - 2D')plt.legend()# Display the 2D plot in a separate windowplt.show()if __name__ == "__main__":input_file_path = "xyz_output.txt"  # Replace with the actual input file pathcoordinates = read_coordinates_from_txt(input_file_path)# Use the first 10 points to define the projection coordinate systemnum_points_for_avg = 20avg_coordinates = coordinates[:num_points_for_avg]avg_latitude, avg_longitude = calculate_avg_coordinates(avg_coordinates)# Project the average latitude and longitude to xy coordinate systemp = 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 systemfirst_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 fileoutput_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