# Autonomous Visual Navigation System Based on a Single Camera for Floor-Sweeping Robot
## 3 Key Technologies
### Dynamic Local Path Planning Algorithm Based on Superpixel Segmentation  
To guide the robot to remove garbage precisely, we proposed a dynamic indoor path planning method for sweeping robots based on superpixel segmentation. The method dynamically processes the depth map in the image sequence, continuously extracts the information of the non-obstacle area, and plans the forward direction. This method is simple in calculation, fast in processing, and can correct the forward movement in time, avoiding producing long-term accumulated errors and improving path planning efficiency.  
Figure 4: Original image and parallax chart of the indoor scene: (**a**) Original image (**b**) parallax map.  
The environmental depth information is shown in Figure 6. The depth map contains the distance information of the scene in Figure 6b, which shows:  
1. Obstacle areas have similar depth information performance due to their spatial relationship so that they can be fitted into superpixel blocks well after superpixel image segmentation.
2. Non-obstacle areas have the largest depth information values and are highly similar in terms of numerical relationships. Therefore, it is easy to cluster into the same superpixel block in superpixel segmentation processing.
3. Objects in different depth environments are divided into different superpixel blocks, and adjacent superpixel blocks are delimited clearly. The relative depth relationships of various objects match well when comparing the images captured in real environments.  
We used the SLIC (Simple Linear Iterative Clustering) [35] superpixel image segmentation algorithm to process the depth images. Based on the segmentation results, which are obtained by the superpixel dynamic local planning algorithm, the non-obstacle areas can be judged as optional forward directions.  
After the superpixel segmentation algorithm processes the depth map, the pixel values within each superpixel block are similar in size to represent the same area or object. The  
Figure 5: RGB-Delta image fusion process.  
Figure 6: Indoor environment with its depth information; (**a**) Original scene image, (**b**) Depth map.  
distribution of the non-obstacle areas in the image can be obtained from the average pixel value within the superpixel block, which can be calculated according to Equation (3):  
\[Value_{unobstacle}=max(\frac{1}{N}\sum_{i=0}^{N}S_{j}), \tag{3}\]  
where \(S_{j}\) denotes the jth superpixel block and \(i\) denotes the pixel points within it. For the depth map after the superpixel segmentation process, the pixel average within each superpixel block is calculated. The one with the largest average value means the region with the largest distance in space, which is the unobstructed region, and the calculation result is shown in Figure 7d.  
Figure 7a indicates the furthest light gray room door and the direction leading to the non-obstacle area. Figure 7b shows the depth information estimated by the indoor depth information sensing module. Figure 7c shows the segmentation results using the SLIC superpixel algorithm, and the farthest area from the camera is accurately segmented. Based on the principle of the maximum pixel mean value of the superpixel block, we segmented and judged the depth map and extracted the area with the maximum mean value, as shown in Figure 7d. It shows that the judgment of the non-obstacle area by the algorithm matches the actual 3D scene.