Autonomous vehicles require in-depth knowledge of their surroundings, making path segmentation and object detection crucial for determining the feasible region for path planning. Uniform characteristics of a road portion can be denoted by segmentations. Currently, road segmentation techniques mostly depend on the quality of camera images under different lighting conditions. However, Light Detection and Ranging (LiDAR) sensors can provide extremely precise 3D geometry information about the surroundings, leading to increased accuracy with increased memory consumption and computational overhead. This paper introduces a novel methodology which combines LiDAR and camera data for road detection, bridging the gap between 3D LiDAR Point Clouds (PCs). The assignment of semantic labels to 3D points is essential in various fields, including remote sensing, autonomous vehicles, and computer vision. This research discusses how to select the most relevant geometric features for path planning and improve autonomous navigation. An automatic framework for Semantic Segmentation (SS) is introduced, consisting of four processes: selecting neighborhoods, extracting classification features, and selecting features. The aim is to make the various components usable for end users without specialized knowledge by considering simplicity, effectiveness, and reproducibility. Through an extensive evaluation of different neighborhoods, geometric features, feature selection methods, classifiers, and benchmark datasets, the outcomes show that selecting the appropriate neighborhoods significantly develops 3D path segmentation. Additionally, selecting the right feature subsets can reduce computation time, memory usage, and enhance the quality of the results.

The purpose of this paper is to offer a unique adaptive path planning framework to address a new challenge known as the Unknown environment Persistent Monitoring Problem (PMP). To identify the unknown events’ occurrence location and likelihood, an unmanned ground vehicle (UGV) equipped with a Light Detection and Ranging (LIDAR) and camera is used to record such events in agriculture land. A certain level of detecting capability must be the distinct monitoring priority in order to keep track of them to a certain distance. First, to formulate a model, we developed an event-oriented modelling strategy for unknown environment perception and the effect is enumerated by uncertainty, which takes into account the sensor’s detection capabilities, the detection interval, and monitoring weight. A mobile robot scheme utilizing LIDAR on integrative approach was created and experiments were carried out to solve the high equipment budget of Simultaneous Localization and Mapping (SLAM) for robotic systems. To map an unfamiliar location using the robotic operating system (ROS), the 3D visualization tool for Robot Operating System (RVIZ) was utilized, and GMapping software package was used for SLAM usage. The experimental results suggest that the mobile robot design pattern is viable to produce a high-precision map while lowering the cost of the mobile robot SLAM hardware. From a decision-making standpoint, we built a hybrid algorithm HSAStar (Hybrid SLAM & A Star) algorithm for path planning based on the event oriented modelling, allowing a UGV to continually monitor the perspectives of a path. The simulation results and analyses show that the proposed strategy is feasible and superior. The performance of the proposed hyb SLAM-A Star-APP method provides 34.95%, 27.38%, 33.21% and 29.68% lower execution time, 26.36%, 29.64% and 29.67% lower map duration compared with the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP.