Efficient Stereo Visual Simultaneous Localization and Mapping for an Autonomous Unmanned Forklift in an Unstructured Warehouse

Abstract

The autonomous navigation of unmanned vehicles in GPS denied environments is an incredibly challenging task. Because cameras are low in price, obtain rich information, and passively sense the environment, vision based simultaneous localization and mapping (VSLAM) has great potential to solve this problem. In this paper, we propose a novel VSLAM framework based on a stereo camera. The proposed approach combines the direct and indirect method for the real-time localization of an autonomous forklift in a non-structured warehouse. Our proposed hybrid method uses photometric errors to perform image alignment for data association and pose estimation, extracts features from keyframes, and matches them to acquire the updated pose. By combining the efficiency of the direct method and the high accuracy of the indirect method, the approach achieves higher speed with comparable accuracy to a state-of-the-art method. Furthermore, the two step dynamic threshold feature extraction method significantly reduces the operating time. In addition, a motion model of the forklift is proposed to provide a more reasonable initial pose for direct image alignment based on photometric errors. The proposed algorithm is experimentally tested on a dataset constructed from a large scale warehouse with dynamic lighting and long corridors, and the results show that it can still successfully perform with high accuracy. Additionally, our method can operate in real time using limited computing resources.

Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion

“…Several technologies exist that can be adopted as providers of external information in the sound [15][16][17], optical [18][19][20][21][22][23], and electromagnetic domains .…”

“…Particularly interesting is the optical domain, which is widely represented in the state-of-the-art [19][20][21][22][23]. The optical domain concerns both solutions with 2D infrared [19] and 3D rotating Light Detection And Ranging (LiDAR) [20] or computer vision (CV) techniques in the visible light domain [21][22][23].…”

“…Particularly interesting is the optical domain, which is widely represented in the state-of-the-art [19][20][21][22][23]. The optical domain concerns both solutions with 2D infrared [19] and 3D rotating Light Detection And Ranging (LiDAR) [20] or computer vision (CV) techniques in the visible light domain [21][22][23]. The first type of solutions can achieve accurate localization performance thanks to the recent improvements in localization and simultaneous localization and mapping (SLAM) algorithms for mobile robots [19].…”

Detection of False Synchronization of Stereo Image Transmission Using a Convolutional Neural Network

“…Stereo-vision is an important part of image research, especially in situations where information about space, the surroundings of the object in the image, or its location is more important than information about the object itself. A lot of research is being carried out in this topics, such as 3D scene reconstruction [8,9], depth detection [10], or autonomous navigation of unmanned vehicles in environments without GPS [11]. Stereo-vision is also used as work validation [12].…”

Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization

“…Despite this adjustment, the feature-based VO methods have been regarded as more suitable approaches than the direct VO methods for cases involving large baselines, fast motions, and varied illumination [14][15][16][17][18][19][20]. From this perspective, studies to combine the advantages of direct VO methods and feature-based VO methods to decrease errors of VO have been conducted [20,21].…”