Ludvig Ericson

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About

Ludvig Ericson is a rising researcher at the intersection of robotics, computer vision, and spatial AI, whose work focuses on enabling machines to understand and predict the structure of indoor environments. His primary research areas include occupancy mapping, floor plan generation, and predictive perception for autonomous navigation. Ericson’s major contribution lies in developing novel methods for inferring unseen architectural features from partial sensor data. In his most-cited work, “Beyond the Frontier: Predicting Unseen Walls From Occupancy Grids by Learning From Floor Plans” (2024, 9 citations), he introduces a technique to predict hidden wall layouts as 2D line segments, significantly advancing a robot’s ability to reason about occluded spaces. He also authored “FloorGenT: Generative Vector Graphic Model of Floor Plans for Robotics” (2022, 2 citations), which applies autoregressive sequence modeling to generate floor plans from a robot’s perspective. Though early in his career, Ericson’s work is gaining traction for its practical implications in improving autonomous exploration and mapping. His research promises to bridge the gap between partial observations and complete spatial understanding, a critical step toward more intelligent and autonomous robotic systems.