Efficient Stereo Visual Simultaneous Localization and Mapping for an Autonomous Unmanned Forklift in an Unstructured Warehouse  
[PERSON]  
1 College of Engineering, South China Agricultural University, Guangzhou 510642, China; [EMAIL_ADDRESS] (F.W. ); [EMAIL_ADDRESS] (E.L.); [EMAIL_ADDRESS] (Y.W. ); Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; [EMAIL_ADDRESS] 1  
[PERSON]  
1 College of Engineering, South China Agricultural University, Guangzhou 510642, China; [EMAIL_ADDRESS] (F.W. ); [EMAIL_ADDRESS] (E.L.); [EMAIL_ADDRESS] (Y.W. ); Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; [EMAIL_ADDRESS] 1  
[PERSON]  
1 College of Engineering, South China Agricultural University, Guangzhou 510642, China; [EMAIL_ADDRESS] (F.W. ); [EMAIL_ADDRESS] (E.L.); [EMAIL_ADDRESS] (Y.W. ); Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; [EMAIL_ADDRESS] 1  
[PERSON]  
2 Clek for the  
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of the environment, it is prone to failure in the most common forklift scene: a warehouse with long corridors. Another disadvantage is that laser sensors are expensive, with some high precision laser sensors costing thousands or even tens of thousands of dollars. In the past decade, we witnessed rapid progress in 3D computer vision technology and the vision based SLAM algorithm, with major changes and breakthroughs in theory and practice [5]. Many methods have been proposed to solve problems related to the accuracy, robustness, and efficiency of this algorithm. Many kinds of cameras have been used with the SLAM algorithm, including monocular, stereo, and RGB-D cameras [9; 10]. In addition, a method that combines a camera and an inertial measurement unit (IMU) has been recently proposed [11], and it achieved robust performance [12]. SLAM technology has also continued to develop, with techniques ranging from the earlier filtering based method to the more recent graph based optimization method, which is currently predominating in this field [13]. The SLAM framework, which has become mature and solid through the long term efforts of researchers, includes front-end visual odometry, back-end nonlinear optimization, mapping, and loop closure. The main task in visual odometry is to estimate the translation and rotation of the camera between adjacent images and build a local map. Because of camera observation error, visual odometry inevitably accumulates errors when it runs for a long time. Back-end optimization receives the camera pose and 3D features of the visual odometer at different times, optimizes them, and obtains the globally consistent trajectory and three-dimensional structure. Loop detection determines whether the robot has reached the previous position. If loop closure is detected, then back-end optimization processes it to acquire a globally consistent map. Generally, mapping is related to the application requirements, such as a sparse point cloud map or dense map. However, there are different ways to realize these modules in different SLAM schemes, especially for visual odometry. Different means of implementation determine the approach to modeling the SLAM problem. The traditional visual SLAM method is typically divided into two groups: the indirect method and the direct method. The indirect method is a well known feature based method that relies on the features extracted from the image: examples include SIFT [14], SURF [15], and ORB [16]. Features can be repeatedly extracted from different images, and the distinctive feature description is more robust to changes in the view angle and illumination. Data association with other frames is carried out through feature matching, and then, the camera pose is solved according to the data association by eight point [17; 18], five point [19; 20], and bundle adjustment. In the feature based method, data association and camera pose estimation are independent of each other.