Kruno Lenac

About

Kruno Lenac is a leading researcher in autonomous robotics, specializing in simultaneous localization and mapping (SLAM) and active perception. His work focuses on enabling robots to intelligently explore and map unknown environments while maintaining accurate self-localization. Lenac’s most influential contribution is his 2017 paper on path planning for active SLAM using the D* algorithm with negative edge weights, which has garnered 124 citations. This work addresses the critical challenge of balancing exploration with revisiting known locations to improve localization accuracy. He further advanced the field with his 2017 study on fast planar surface 3D SLAM using LIDAR (47 citations), demonstrating efficient mapping in structured environments. Lenac also made significant theoretical contributions to SLAM back-ends with his 2018 paper on the exactly sparse delayed state filter on Lie groups (19 citations), which preserves the advantages of classic filtering approaches while enabling long-term pose graph optimization. His research has practical implications for autonomous navigation in complex, unknown spaces, and his work on active SLAM for accurate coverage mapping (2015) continues to influence modern exploration algorithms.

Research Focus

Key Achievements

Top Papers

1. 1
   Path Planning for Active SLAM Based on the D* Algorithm With Negative Edge Weights

   124 citations · 2017
2. 2
   Fast planar surface 3D SLAM using LIDAR

   47 citations · 2017
3. 3
   Exactly sparse delayed state filter on Lie groups for long-term pose graph SLAM

   19 citations · 2018
4. 4
   Fast Active SLAM for Accurate and Complete Coverage Mapping of Unknown Environments

   12 citations · 2015
5. 5
   Revival of filtering based SLAM? Exactly sparse delayed state filter on Lie groups

   4 citations · 2017

Key Collaborators

Contact & Links