Papers
11
Total Citations
72
H-Index
5
About
Johann Laconte is a robotics researcher specializing in autonomous navigation, 3D mapping, and risk-aware path planning, with a particular focus on challenging real-world environments. His most influential work, "Kilometer-Scale Autonomous Navigation in Subarctic Forests" (2022, 24 citations), addresses the formidable challenges of wintertime off-road autonomy, including unreliable GNSS signals, low visual contrast, and dynamic environmental conditions. Complementing this, his geometry-preserving sampling method based on spectral decomposition (2020, 16 citations) advances efficient large-scale 3D lidar mapping by retaining critical geometric features without sacrificing computational practicality. Laconte has made meaningful contributions to safer robot navigation through his occupancy mapping and risk-aware frameworks, developing novel probabilistic approaches that move beyond simple collision avoidance toward nuanced harm estimation. His work on SLAM robustness against gyroscope saturation and ICP-based localization certification further demonstrates his commitment to reliable autonomy under adverse conditions. More recently, he has extended these principles into agricultural robotics, addressing collision-aware traversability for sustainable farming applications. Across more than ten publications and accumulating over 70 citations, Laconte's research consistently bridges theoretical rigor with the messy realities of field deployment, making his work essential reading for researchers tackling autonomous systems in unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 4Extrinsic calibration for highly accurate trajectories reconstruction6 citations · 2023
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- 8Risk-Aware Navigation for Mobile Robots in Unknown 3D Environments2 citations · 2023
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