Kuya Takami

About

Kuya Takami is a leading researcher in autonomous robotics, with a focus on aerial and ground vehicle navigation in unknown environments. His work integrates Bayesian probabilistic mapping, stochastic motion planning, and sensor fusion to enable robots to explore, map, and patrol complex spaces with minimal human intervention. Takami’s most influential contribution is his development of exact occupancy probability mapping for autonomous quadrotors, allowing drones to efficiently explore 3D environments while minimizing map uncertainty—a method that has garnered 21 citations. He also advanced autonomous car navigation through grid-based scan-to-map matching, demonstrated on a modified Toyota Prius, and pioneered non-field-of-view sound source localization using acoustic and optical sensors for mobile robots. His research on multi-robot patrol of structured indoor environments further showcases his impact on collaborative autonomous systems. With a total of over 46 citations across his top papers, Takami’s work bridges theory and practice, offering robust solutions for real-world exploration, mapping, and navigation challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   Autonomous Quadrotor 3D Mapping and Exploration Using Exact Occupancy Probabilities

   21 citations · 2018
2. 2
   Autonomous Exploration with Exact Inverse Sensor Models

   11 citations · 2017
3. 3
   Map-Based Navigation of an Autonomous Car Using Grid-Based Scan-to-Map Matching

   5 citations · 2015
4. 4
   Non-field-of-view sound source localization using diffraction and reflection signals

   5 citations · 2016
5. 5
   Bayesian Mapping-Based Autonomous Exploration and Patrol of 3D Structured Indoor Environments with Multiple Flying Robots

   4 citations · 2019

Key Collaborators

Contact & Links