Papers
8
Total Citations
411
H-Index
7
About
Julio A. Placed is a robotics researcher whose work sits at the intersection of autonomous navigation, simultaneous localization and mapping (SLAM), and intelligent robot decision-making. He has emerged as a leading voice in the field of Active SLAM — the challenge of enabling robots to autonomously plan their motion while constructing accurate maps of unknown environments. His landmark survey on Active SLAM (2023, 290 citations) has become an essential reference for the community, synthesizing over three decades of progress and charting new frontiers for the field. Beyond survey contributions, Placed has made significant methodological advances, including pioneering the application of deep reinforcement learning to Active SLAM (2020, 46 citations) and revealing fundamental connections between graph theory and optimal experimental design to accelerate robotic exploration (2021, 25 citations). His ExplORB-SLAM framework further demonstrates his ability to translate theoretical insights into practical systems. More recently, his work has expanded into robust range-inertial SLAM across diverse environments and principled stopping criteria for autonomous exploration. Across his growing body of work, Placed consistently bridges rigorous mathematical foundations with real-world robotic deployment, making him an influential figure for students and researchers in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Deep Reinforcement Learning Approach for Active SLAM46 citations · 2020
- 3Fast Autonomous Robotic Exploration Using the Underlying Graph Structure25 citations · 2021
- 4ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology17 citations · 2022
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