## 6 Obstacle Detection and Analysis Using Different Types of LiDAR  
There are many types of LiDAR available on the market that have different parameters, such as measuring range, field of view (horizontal and azimuth), the number of measuring channels (the number of emitters and receivers of laser light), and the arrangement of sensor elements. We evaluated two types of 3D LiDAR to record the experimental data: Ouster OS0-32 and Velodyne VLP-16. The Velodyne LiDAR system was mounted in the front of the vehicle to detect obstacles ahead. Ouster OS0-32 LiDAR was installed on the roof of the vehicle to record data for spatial mapping purposes.  
The influence of the scanner location on the quality of individual object imaging is presented in Figure 9, with a human standing near the road marked on the 3D scan. The Velodyne LiDAR, despite a smaller field of view in the elevation range (22.5 degrees) and fewer measurement channels, performed better regarding spatial imaging of the human body compared to the Ouster LiDAR, which was characterized by and angle of view in the elevation of 90 degrees with 32 measurement channels. Other objects such as trees behind the man are also more clearly depicted. The point cloud from the Ouster OS0-32 scanner representing surrounding objects looks more chaotic, which makes it difficult to find the distribution of points characteristic of a vertical object with a cross section similar to a circle. Data received from Velodyne LiDAR allows for more precise indication of such objects and can be excellent input as a training dataset for the neural network to support automatic detection of objects in the 3D point cloud.  
During all tests in the off-road environment, additional objects were placed in the field of view of the validated sensors. The main aim was to evaluate the impact of vegetation  
Figure 9: Spatial imaging of a man (**a**) and trees (**b**) using the Ouster OS0-32 and Velodyne LiDAR systems with referential image from the ZED 2i camera.  
on obstacle detection. A hidden barrel about 1 m high can be found in Figure 10 located on the left side under the branches of a coniferous tree. The laser scanner located in the front of the vehicle (Velodyne VLP-16) provided better imaging (similar to the previously described analysis of a human).  
In the case of building a spatial map of the environment, data from Ouster LiDAR (Figure 11) are more useful due to the scanner location and the larger field of view. The data from a scanner with a limited field of view may not provide a lot of detail, but they can still be used to complete the overall dataset due to the density of measurement points. Determining the translation matrix between the scanners, it is possible to obtain a very accurate spatial map covering the surroundings. For this purpose, it is necessary to use auxiliary information from an inertial system and correlation details of both scans in the time domain. One of the LiDAR systems used in this research (Ouster) is equipped with a built-in IMU system to provide required data to the computational unit.  
The determination of the translation matrix and the construction of maps from 3D scans are tasks for further work and investigation regarding perception system improvement for unmanned vehicles operating in off-road environments. Additionally, machine learning algorithms can be trained on the combined dataset to improve the accuracy of object detection.  
Figure 10: Observation of the barrel (**a**) using Velodyne VLP-16 and Ouster OS0-32 LiDAR with a reference image from the ZED 2i camera.