Path Segmentation from Point Cloud Data for Autonomous Navigation

Abstract

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.

Analysis of Transportation Systems for Colonies on Mars

“…The exploration of alternative transportation modes, such as hyperloops, drones, and rovers, complements the existing literature by offering a holistic view of the potential transportation landscape on Mars. The integration of RL algorithms for autonomous navigation builds upon the works of [8][9][10][11]15], showcasing the potential for advanced autonomy in enhancing the operational efficiency and adaptability of these systems in the challenging Martian environment. This contributes to the growing interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to support Martian exploration and colonization efforts.…”

“…This approach underlines the critical role of rovers in conducting detailed geological surveys, atmospheric analysis, and search for signs of past life, thereby providing invaluable data to inform future colonization efforts. Furthermore, the advancement in autonomous navigation technologies, as discussed in [9], highlights the evolving capabilities of rovers to traverse and analyze the Martian terrain independently, significantly expanding the scope of exploration beyond landing sites.…”

“…The simulation employs a grid-based representation of the Martian surface, where each cell can be free, occupied by an obstacle, or part of the path determined by the autonomous navigation algorithm [9,11,16]. A feature density parameter controls the distribution of obstacles across the grid, reflecting the variable complexity of the Martian terrain.…”

Synergizing Cloud in Autonomous Vehicles.

“…Leveraging the edge node computational power for decentralized peer to vehicle detection also assures that the latency is within the expected levels and that the quality of data is better. [5] Autonomous navigating which is one of the basic elements of self-driving vehicles is responsible for them being able to cruise safe and smoothly through the different surrounds. Path segmentation is a crucial feature in the autonomous navigation as it provides the required information for crafting path from the point cloud data.…”

Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

“…One of the well-known sensors used for AV navigation is LiDAR. For example, Krishnamoorthi et al [10] considered a navigation and localization system consisting of LiDARs and on-board cameras. The system performed path segmentation from point cloud data, which could be further used in computer vision and AV localization.…”