Nowadays, there has been a growing interest in the use of mobile robots
for various applications,where the analysis of the operational environment is a crucial
component to conduct our special tasks ormissions. Themain aimof thiswork
was to implement artificial intelligence (AI) for object detection and distance estimation
navigating the developed unmanned platform in unknown environments.
Conventional approaches are based on vision systems analysis using neural networks
for object detection, classification, and distance estimation. Unfortunately,
in the case of precise operation, the used algorithms do not provide accurate data
required by platforms operators as well as autonomy subsystems. To overcome this
limitation, the authors propose a novel approach using the spatial data from laser
scanners supplementing the acquisition of precise information about the detected
object distance in the operational environment.
In this article, we introduced the application of pretrained neural network
models, typically used for vision systems, in analysing flat distributions of LiDAR
point cloud surfaces. To achieve our goal, we have developed software that fuses
detection algorithm(based on YOLO network) to detect objects and estimate their
distances using theMiDaS depth model. Initially, the accuracy of distance estimationwas
evaluated through video streamtesting in various scenarios. Furthermore,
we have incorporated data from a laser scanner into the software, enabling precise
distance measurements of the detected objects.
The paper provides discussion on conducted experiments, obtained results,
and implementation to improve performance of the described modular mobile
platform.
