Jens Hensler

HTWG Hochschule Konstanz - Technik, Wirtschaft und Gestaltung

Papers

Total Citations

H-Index

About

Jens Hensler is a researcher whose work sits at the intersection of computer vision and autonomous navigation, with a particular focus on enabling intelligent systems to perceive and move through indoor environments. His most influential contribution, the 2010 paper "Real-Time Door Detection Based on AdaBoost Learning Algorithm," has garnered 35 citations and established a practical, efficient method for robots to identify doorways—a critical capability for indoor wayfinding and human-robot interaction. By applying the AdaBoost machine learning framework to real-time visual detection, Hensler helped bridge the gap between theoretical computer vision and deployable robotic systems. His subsequent work, "Using Quadtrees for Realtime Pathfinding in Indoor Environments" (2011), further advanced the field by introducing a spatial data structure that dramatically reduces computational overhead for route planning in complex, dynamic spaces. Though his publication record is focused, it demonstrates a clear, applied vision: equipping mobile robots with the perceptual and planning tools they need to navigate the built world. Hensler’s research remains a valuable reference for engineers and students working on low-latency perception and autonomous indoor mobility.