Mario A. V. Saucedo
Papers
10
Total Citations
45
H-Index
4
About
Mario A. V. Saucedo is a robotics researcher whose work spans autonomous navigation, multi-robot systems, and perception for challenging environments. His research is particularly focused on enabling robots to operate effectively in complex, unstructured, and subterranean settings — areas where conventional approaches frequently break down. Among his most notable contributions is RecNet, an invertible point cloud encoding system that addresses simultaneous place recognition and map sharing in multi-robot systems, earning 9 citations since its 2024 publication. His STAGE exploration planner, which introduces traversability-aware, graph-based navigation capable of handling dynamic environmental changes, has similarly drawn strong community interest with 8 citations. Saucedo has also advanced perception under adversarial conditions, proposing a LiDAR and event camera fusion framework for human tracking in low-light and high-contrast subterranean environments, accumulating 7 citations. His broader portfolio reflects a coherent vision: equipping autonomous systems with richer environmental understanding through traversability estimation (EAT), semantic scene graphs (Belief Scene Graphs), and commonsense affordance reasoning. With a cumulative citation count exceeding 45 across recent publications alone, Saucedo's work is gaining meaningful traction, making him a researcher to watch in the fields of field robotics and autonomous exploration.
Research Focus
Key Achievements
Top Papers
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- 4EAT: Environment Agnostic Traversability for reactive navigation7 citations · 2023
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