单目slam

 

 

程序输入需要  

1 真值是为为了最后的评估

2 深度图也是为了评估或者跑深度的

 

运行

source /home/dongdong/1sorftware/1work/yes/etc/profile.d/conda.sh
conda activate MonoGS
(MonoGS) dongdong@dongdong:~/2project/MonoGS$ python slam.py --config configs/mono/tum/fr3_office.yaml

1 脚本生成 文件名字列表 程序依靠着输入图像
/home/dongdong/2project/MonoGS/utils/generate_txts.py --dataset tum --scene fr3_office --output tum/fr3_office.txt
1-1 修改图像文件夹
base_folder  = '/media/dongdong/Elements/项目/ehang_/2TestData/2TestData/ehang3'
1-2 修改保存名字 经度
f.write(f"{idx:04d} {filename_with_path}\n")

2 修改配置文件的数据地址
  dataset_path: "/home/dongdong/2project/0data/RTK/data_3_jianda/300_map_3pm"

3 修改参数
/home/dongdong/2project/MonoGS/configs/mono/tum/base_config.yaml
迭代次数
init_itr_num: 1050
tracking_itr_num: 1500 #
mapping_itr_num: 1500  #150

开始运行
cd ~/2project/MonoGS/
python slam.py --config configs/mono/tum/mytrial_rtk.yaml

　　

配置文件

inherit_from: "/home/dongdong/2project/MonoGS/configs/mono/tum/base_config.yaml"

Dataset:
  dataset_path: "/home/dongdong/2project/0data/RTK/data_4_city/300_map_2pm"
  Calibration:
    fx: 1193.7076128098686
    fy: 1193.1735265967602
    cx: 910.8785687265895
    cy: 602.174293145834
    k1: 0.0
    k2: 0.0
    p1: 0.0
    p2: 0.0
    k3: 0.0
    width: 1805
    height: 1203
    distorted: False

　　

脚本

从原始真值中提取和图片一一对应的位姿

原来的真值是时间戳，对应不上

 

 

手动对其 找最近的时间戳

 

import os
import numpy as np

def extract_timestamp(filename):
    """从文件名中提取时间戳（去掉.png后缀）"""
    return float(filename.replace('.png', ''))

def read_ground_truth(file_path):
    """读取ground truth文件，返回时间戳和位姿数据的列表"""
    ground_truth_data = []
    with open(file_path, 'r') as f:
        for line in f:
            # 跳过注释行
            if line.startswith('#'):
                continue
            # 分割行数据
            parts = line.strip().split()
            if len(parts) >= 8:  # 确保有足够的数据
                timestamp = float(parts[0])
                timestamp_s=str(parts[0])
                tx, ty, tz = map(float, parts[1:4])
                qx, qy, qz, qw = map(float, parts[4:8])
                ground_truth_data.append((timestamp, tx, ty, tz, qx, qy, qz, qw,timestamp_s))

    # 按时间戳排序
    ground_truth_data.sort(key=lambda x: x[0])      
    return ground_truth_data

def find_closest_ground_truth(target_timestamp, ground_truth_data):
    """在ground truth数据中找到时间戳最接近的那一行"""
    # 将ground truth数据转换为numpy数组以便计算
    timestamps = np.array([data[0] for data in ground_truth_data])
    # 计算时间差
    time_diffs = np.abs(timestamps - target_timestamp)
    # 找到最小差值的索引
    closest_idx = np.argmin(time_diffs)
    return ground_truth_data[closest_idx]

def process_images(rgb_folder, ground_truth_file, output_file):
    """处理图片和ground truth数据，生成新的输出文件"""
    # 读取ground truth数据
    ground_truth_data = read_ground_truth(ground_truth_file)
    
    # 获取所有图片文件名
    image_files = [f for f in os.listdir(rgb_folder) if f.endswith('.png')]
    


    # 提取每个图片的时间戳并创建(时间戳, 文件名)的元组列表
    image_timestamps = []
    for image_file in image_files:
        timestamp = extract_timestamp(image_file)
        image_timestamps.append((timestamp, image_file))

    # 按时间戳排序（升序）
    image_timestamps.sort(key=lambda x: x[0])  

    # 打开输出文件
    with open(output_file, 'w') as out_f:
        # 写入表头（可选）
        #out_f.write("# image_filename tx ty tz qx qy qz qw original_timestamp\n")
        
        for image_timestamp, image_file in image_timestamps:


            # 找到最接近的ground truth
            closest_gt = find_closest_ground_truth(image_timestamp, ground_truth_data)
            
            # 写入输出文件
            out_line = f"{image_file} {closest_gt[1]} {closest_gt[2]} {closest_gt[3]} {closest_gt[4]} {closest_gt[5]} {closest_gt[6]} {closest_gt[7]} {closest_gt[8]}\n"
            out_f.write(out_line)

# 主程序
if __name__ == "__main__":
    # 文件夹和文件路径
    data="rgbd_dataset_freiburg1_desk"
    rgb_folder = data+'/rgb'  # 图片文件夹路径
    ground_truth_file = data+'/groundtruth.txt'  # ground truth文件路径
    output_file = data+'/gnss.txt'  # 输出文件路径
    
    # 处理数据
    process_images(rgb_folder, ground_truth_file, output_file)
    
    print(f"处理完成，结果已保存到 {output_file}")