# UWB-Based Human-Following System with Obstacle and Crevasse Avoidance for Polar-Exploration Robots
## 4 Local Obstacle Map Building  
KAREX utilizes four LiDAR sensors to acquire information about obstacles surrounding the robot and displays this information on a grid map-based local obstacle map. The local obstacle map is generated through the process depicted in Figure 5.  
Figure 4: Procedure of the position estimation for explorer with UWB tag.  
As shown in Figure 5, the local obstacle map building begins by acquiring a 3D point cloud from the multi-channel LiDAR. The minimum and maximum height ranges of the point cloud are then set, and the point cloud within this range is projected onto the 2D ground plane to convert it into 2D plane data. (In terms of the ROS platform, this can be described as changing the data from the point cloud format [29] to the scan format [30]). This process simplifies the representation of obstacles on the grid map and reduces the amount of computational data. The scan information obtained from each LiDAR is then integrated into a single scan dataset (Hereafter, this will be referred to as obstacle_scan).  
Before the obstacle_scan is marked on the grid map, this paper assumes that while there might be obstacles between the explorer with tags and the robots equipped with anchors, a situation where the path is completely blocked and impassable does not occur. This assumption is used to generate a local obstacle map. The robots following the explorer maintain a close distance, and the explorer passes through areas that are wide enough for the robots to follow. Of course, unexpected situations such as a suddenly revealed crevasse, as mentioned in the introduction, may prevent the robots from moving in the direction of the explorer. However, even under the stated assumption, the robots can avoid such obstacles by using the local path described later.  
Under this assumption, the local obstacle map is generated as shown in Figures 6 and 7. As depicted in Figure 6, the local obstacle map is created on an n \(\times\) n grid map where the center of the map is the robot's position. The local obstacle map proposed in this paper for collision avoidance while following the explorer does not display obstacles or collision regions within m pixels from the edges of the grid map, unlike other existing local obstacle maps. This reflects the earlier assumption that there is enough space for the robot to move between the explorer and the robot.  
Figure 5: Process of generating a local obstacle map.  
Figure 6: The proposed concept of local obstacle map.  
Figure 7 compares the conventional local obstacle map with the local obstacle map reflecting the assumptions of this paper. As shown in Figure 7, the conventional obstacle map displays obstacles up to the edges of the grid map. In contrast, the proposed local obstacle map does not display obstacles at the edges. The resulting non-obstacle area supports the creation of a local path that can follow the tag, even if an obstacle appears at the sub-goal location during sub-goal generation on the regional map described in the next chapter. (Further details are provided in Section 5).