3D Map Generation for Indoor Non-Manhattan World Environments

Abstract

Accurate mapping of indoor environments is crucial for various real-world applications, including search and rescue operations, as well as home robotics. Utilizing small, cost-effective drones provides significant advantages over traditional methods for mapping confined and unknown spaces. This paper advances existing robotics mapping research by presenting a novel algorithm for three-dimensional (3D) indoor environment mapping, particularly in non-Manhattan World settings. Our approach addresses the limitations of previous methods by introducing an efficient algorithm that filters noise, identifies structural corners using breadth-first search and convolution, and constructs nonintersecting polygons for environment mapping. Experimental results validated the effectiveness of our solution in producing reliable indoor maps where previous methods fail, showing improvements in automatic wall detection, noise resilience, and handling of more complex shapes and scenarios.