帮我写一份go-icp配准算法的Python代码，输入的是三维点云数据的txt格式，输出均方根误差，旋转误差（使用arccos函数计算）、平移误差以及配准时间，尽可能详细点。

在Python中编写Go-ICP（Iterative Closest Point）配准算法处理三维点云数据涉及到一些数学计算，包括点匹配、距离计算、优化迭代等步骤。以下是一个简化的示例代码，假设已经安装了numpy和scipy库：

import numpy as np
from scipy.spatial.distance import cdist
from math import cos, radians, arccos

def read_points_from_txt(filename):
    with open(filename, 'r') as file:
        points = []
        for line in file:
            point = [float(coord) for coord in line.strip().split()]
            points.append(point)
    return np.array(points)

def icp(source, target, init_transform=None, max_iterations=100, tolerance=1e-6):
    if not init_transform:
        # 使用随机初始化
        transform = np.eye(4)
    else:
        transform = init_transform

    prev_error = float('inf')
    for _ in range(max_iterations):
        transformed_source = np.dot(transform[:3, :3], source.T) + transform[:3, 3].reshape(-1, 1)
        
        dists = cdist(transformed_source, target)
        closest_idx = np.argmin(dists, axis=1)

        source_correspondences = source[closest_idx]
        target_correspondences = target

        # 计算平均偏差向量和平移
        mean_diff = np.mean(target_correspondences - source_correspondences, axis=0).reshape(1, 3)
        rotation_matrix, _ = cv2.Rodrigues(np.mean(source_correspondences[:, :3] * (target_correspondences[:, :3] - source_correspondences[:, :3]).T, axis=0))

        # 更新变换矩阵
        transform[:3, :3] = np.dot(rotation_matrix, transform[:3, :3])
        transform[:3, 3] -= mean_diff

        current_error = np.mean(dists)
        if abs(prev_error - current_error) < tolerance:
            break
        prev_error = current_error
    
    return transform, current_error

# 示例用法
source_data = read_points_from_txt('source_cloud.txt')
target_data = read_points_from_txt('target_cloud.txt')

transform, error = icp(source_data, target_data)

# 计算并输出误差值
rms_error = np.sqrt(error)
rotation_angle_radians = arccos((np.trace(rotation_matrix) - 1) / 2)
rotation_error_degrees = rotation_angle_radians * 180 / np.pi
translation_error = np.linalg.norm(transform[:3, 3])

print(f"均方根误差 (RMS): {rms_error}")
print(f"旋转误差 (弧度): {rotation_angle_radians}, 度: {rotation_error_degrees}")
print(f"平移误差: {translation_error}")

# 输出配准时间，这取决于实际运行环境
import time
start_time = time.time()
...
end_time = time.time()
alignment_time = end_time - start_time
print(f"配准时间: {alignment_time} 秒")

注意这个例子使用了`opencv-python`库中的`Rodrigues`函数来从旋转偏差转换到旋转矩阵，你需要先安装这个库。此外，这是一个简化版本，实际应用可能需要处理更多的细节，如异常处理、更复杂的优化策略等。