## 1 Introduction  
Techniques used to estimate the camera pose, with respect to an object, are widely employed in many fields, such as industrial control, object tracking, vehicle navigation, etc. However, a recurring issue is the recovery of the six unknown degrees of freedom of the pose. Various optical 3D pose estimation techniques have been developed in the past few years, such as laser scanning, binocular vision, structured light, and deep learning methods. Laser scanning employs the triangulation relationship in optics [1], however the conventional scanning measurement technique is not always fast enough. Binocular stereo vision technique observes one scene from different viewpoints [2] to recover the depth information of the scene. The basic principle of binocular stereo vision is similar to the mechanism of human eyes. The structured light method projects coded light onto the object [3] and uses the deformed fringes recorded from the surface of the object to calculate the depth and the pose information. Recently, there has been an increased research interest in the deep learning method, where oriented 3D bounding boxes of physical objects can be detected by using a convolutional neural network given 3D sensor data [4]. However, this statistical-learning method requires a large number of training samples [5; 6; 7; 8]. Electric vehicle battery charging always time consuming, meanwhile the process of changing an automatic battery takes less than 5 min. The key issue surrounding the automatic battery-replacement system is how to precisely estimate the pose of the camera in respect to the battery. Pose-estimation algorithm, based on feature matching and Perspective-n-Point (PnP) method, is an attractive technique since new objects can easily be learned online. The single-shot method is stable and computationally efficient, showing great potential for time-critical applications. This method needs fewer cameras than the binocular stereo vision technique, which means that the system incurs lower-cost and is more compact. However, only the front side of the battery can be captured, which results in a planar pose-estimation problem, and the accuracy of the pose-estimation results will slightly decrease when considering the yaw angle. In this paper, laser rangefinders are utilized to detect the distance between the camera and the battery with higher precision, providing more precise yaw angle information. We focus on solving the problem for 3D pose estimation of textured objects with a monocular camera and laser rangefinders. The pose estimation aims to calculate the 3D position and orientation of objects via a camera view-point. With oriented Feature from Accelerated Segment Test(FAST) [9] detecting the keypoints, and rotated BRIEF [10] extracting the descriptors, the ORB method is a very fast binary descriptor which is rotation invariant and resistant to noise [11]. With the matched 2D coordinates of the keypoints in the image, and the corresponding 3D coordinates obtained in real world, the pose of the battery will be calculated on the basis of the theory of the pinhole camera model. The Fast Library for Approximate Nearest Neighbors (FLANN) [12] technique is one of the most popular matching techniques for ORB key-points and descriptors because of its efficiency and simplicity. However, this method is based on a global searching technique, the number of incorrect correspondences grows significantly if there are a large number of keypoints with similar descriptors. This paper presents an improved matching technique based on a spatial consistency searching method to determine the correct correspondences. Finding the corresponding keypoints is still based on the hamming distance, but is improved by combining cluster searching and an iterative reprojection algorithm. Before keypoints matching process, the center-based spatial consistency determining step is executed to narrow the search area for every keypoint.