Jun Konno
Papers
1
Total Citations
2
H-Index
1
About
Jun Konno is a robotics researcher focused on advancing autonomous navigation in dynamic human environments. His primary research areas include 3D Simultaneous Localization and Mapping (SLAM), sensor fusion, and perception systems for mobile robots. Konno’s major contribution lies in improving the accuracy of 3D-SLAM by integrating image recognition from web cameras with 3D-LiDAR point cloud data to detect and remove moving objects—a critical challenge for robots operating in crowded spaces like restaurants and airports. His most-cited work, "Improvement of 3D-SLAM Accuracy by Removing Moving Objects on 3D-LiDAR Point Cloud Using Image Recognition in Web Camera" (2022), has garnered 2 citations and demonstrates a practical, low-cost approach to enhancing map reliability. This research directly addresses the need for robust pre-mapping in autonomous systems, paving the way for safer and more efficient robot navigation. Konno’s work is particularly notable for its emphasis on real-world applicability, bridging the gap between computer vision and LiDAR-based perception. As autonomous mobility continues to expand into human-centric spaces, his contributions offer valuable insights for researchers and engineers developing next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1