# Autonomous Visual Navigation System Based on a Single Camera for Floor-Sweeping Robot
## 2 Framework of Autonomous Visual Navigation System  
On account of the unique advantage of the camera in visual navigation, we conceptualized a single-camera-based sweeping robot system using cloud computing, as shown in Figure 1. Since the system used wireless transmission and a cloud computing model, it did not require additional high-performance computing resources. As a result, it reduces the hardware cost and provides the possibility of implementing cloud control of the robot.  
The system will significantly reduce the number and type of sensors for the new sweeping robot. The full utilization of single-camera visual information is the key to achieving an autonomous visual navigation system for the robot. This autonomous visual navigation system needs to have at least the following three functions:  
Figure 1: Sweeping robot using cloud computing.  
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2. Garbage recognition can determine the relative orientation between the robot and the garbage.
3. Estimation of the distance between the robot and furniture under monocular vision can be used for indoor environment perception of the robot.
4. Distinguishing the obstacle areas from the clear areas forms the basis for dynamic local path planning.  
For the above three system functions, we proposed the technical process shown in Figure 2, which formed the basic technical framework of the autonomous visual navigation system.  
In this framework, the target detection algorithm SSD [20] (Single Shot MultiBox Detector) is used to identify garbage to actively avoid blind cleaning. To achieve distance perception between the robot and obstacles, we proposed an improved method based on a self-encoder fully convolutional neural network to transform the environment perception problem into a monocular vision depth estimation problem. Based on the environment depth estimation, the depth information map is partitioned into feasible and infeasible regions using the superpixel segmentation algorithm to guide the indoor dynamic local path planning. We only require the image information from a single camera and use intelligent technologies such as deep neural networks in our solution. It utilizes cloud computing and centralized computing to reduce the production cost of the sweeping robot while also extending functions such as cloud monitoring and cloud cleaning. The workflow of the autonomous vision navigation sweeping robot is shown in Figure 2.  
In Figure 3, the process of the autonomous visual navigation method of the sweeping robot based on superpixel segmentation can be summarized in the following steps:  
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2. Inspection state. The robot rotates the direction randomly to sample images and detect garbage.
3. Targeting. After detecting the garbage, the robot adjusts its direction so that the cleaning target is in the robot's view.
4. Environment perception. The robot performs environmental depth perception based on its position and calculates the distance from the robot to the obstacles and the distance from the robot to the targets.  
Figure 2: The workflow of the autonomous visual navigation sweeping robot.  
Dynamic local path planning. The robot uses a superpixel image segment algorithm to process the image, determine the through and obstacle areas in the depth information map, and plan the way forward.
5 Directional calibration. As the robot moves forward, it continuously acquires images, re-plans the next step forward, and calibrates the forward direction in time.