Daniel Duberg
Papers
7
Total Citations
382
H-Index
5
About
Daniel Duberg is a leading robotics researcher specializing in autonomous exploration, 3D mapping, and dynamic environment perception. His most impactful contribution is the development of efficient exploration planning algorithms, most notably in his 2019 paper "Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments," which has garnered 221 citations. This work introduced a novel approach combining Frontier Exploration Planning with Receding Horizon Next-Best-View Planning, significantly improving how robots navigate unknown spaces. Duberg further advanced the field with UFOExplorer (61 citations), a fast and scalable sampling-based exploration method that excels in large environments through a graph-based planning structure. His work on dynamic awareness is equally significant, with DUFOMap (2024) and the Dynamic Points Removal Benchmark (2023) addressing the critical challenge of handling moving objects in point cloud maps. Additionally, his UFOMap framework provides an efficient probabilistic 3D mapping solution that intelligently handles unknown space, while his exploration of Signal Temporal Logic for guiding autonomous navigation demonstrates his innovative approach to incorporating formal specifications into robotic behavior.
Research Focus
Key Achievements
Top Papers
- 1Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments221 citations · 2019
- 2
- 3Guiding Autonomous Exploration With Signal Temporal Logic34 citations · 2019
- 4A Dynamic Points Removal Benchmark in Point Cloud Maps32 citations · 2023
- 5DUFOMap: Efficient Dynamic Awareness Mapping28 citations · 2024
- 6
- 7Long-Term Exploration in Unknown Dynamic Environments2 citations · 2021