This paper presents an innovative environment representation technique for mobile robots, incorporating obstacle detection within their operational space. Leveraging the fuzzy signature method, this approach uses quadtrees for efficient data organization. A set of fuzzy rules evaluates feature points to ascertain the relevance of identified obstacles. These points and their fuzzy associations are systematically arranged using a quadtree structure. The environmental model is reconstructed by traversing this tree and applying the established fuzzy rules. This paper has achieved a high-resolution grid representation of 0.1m within a 20m×20m area. Notably, the inference operation completes in just 0.5 ms, underscoring the method’s efficiency. Additionally, the technique is optimized for low memory consumption, demonstrating effective resource management even on older PCs, such as an Intel Core Duo 2 with 16 GB RAM. This representation is designed to support advanced robotic functions, such as obstacle navigation in a distributed computing environment.