A Lightweight Approach to Efficient Multimodal 2D Navigation and Mapping: Unified Laser-Scans as an Alternative to 3D Methods

In this paper, we propose a novel approach for efficient 2D navigation using a multimodal sensor fusion technique. Our method focuses on merging data from multiple sensors, such as LiDARs, cameras, and ultrasonic sensors, into a unified Laser-Scan, which serves as a foundation for faster and more lightweight navigation. By fusing sensor data at the Laser-Scan level, our approach enables the use of basic 2D Simultaneous Localization And Mapping (SLAM) algorithms for mapping tasks, or any others Laser-Scan based features, while still benefiting from the rich information provided by multimodal 3D inputs. This results in a more computationally efficient solution compared to traditional 3D methods that rely on depth points or full multimodal SLAM systems. Our experimental results demonstrate that the proposed approach achieves comparable accuracy in mapping and localization while significantly reducing computational complexity and processing time. This research offers a promising alternative for real-time 2D navigation in resource-constrained autonomous systems, such as drones or any small unmanned vehicles.