下面是一个解决ArUco码位姿估计问题的示例代码，使用了OpenCV的solvePnP函数：

import cv2
import numpy as np

# ArUco参数
aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_4X4_250)
parameters =  cv2.aruco.DetectorParameters_create()

# 获取ArUco码角点和ID
def get_aruco_corners(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    corners, ids, _ = cv2.aruco.detectMarkers(gray, aruco_dict, parameters=parameters)
    return corners, ids

# 3D目标点坐标
target_points_3d = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]], dtype=np.float32)

# 相机内参
camera_matrix = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)
dist_coeffs = np.zeros((4,1), dtype=np.float32) # 如果有畸变系数，填入实际值

# 读取图像
image = cv2.imread('image.jpg')

# 检测ArUco码
corners, ids = get_aruco_corners(image)

# 位姿估计
if len(ids) > 0:
    rvec, tvec, _ = cv2.aruco.estimatePoseSingleMarkers(corners, 0.1, camera_matrix, dist_coeffs)
    for i in range(len(ids)):
        # 绘制坐标系
        cv2.aruco.drawAxis(image, camera_matrix, dist_coeffs, rvec[i], tvec[i], 0.1)
        # 绘制边界框
        cv2.aruco.drawDetectedMarkers(image, corners)

        # 通过solvePnP获取位姿
        _, rvec, tvec = cv2.solvePnP(target_points_3d, corners[i], camera_matrix, dist_coeffs)
        print('ID:', ids[i][0])
        print('Rotation Vector:')
        print(rvec)
        print('Translation Vector:')
        print(tvec)

cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，您需要根据实际情况调整相机内参（fx，fy，cx，cy）和ArUco码的大小（0.1）。您还需要正确安装OpenCV库并导入所需的模块。