Juan Nieto

About

Juan Nieto is a prominent robotics and autonomous systems researcher whose work spans mobile robot navigation, unmanned aerial vehicles (UAVs), simultaneous localization and mapping (SLAM), and precision agriculture. His research has made transformative contributions to how robots perceive, plan, and act in complex, real-world environments. Nieto is perhaps best known for pioneering data-driven approaches to autonomous navigation, including an end-to-end motion planning model that maps raw sensor data directly to steering commands (426 citations), and a reinforced imitation learning framework that dramatically improves sample efficiency for mapless navigation (198 citations). His work on informative path planning for UAVs — enabling robots to autonomously acquire high-value data in unknown environments — has drawn nearly 500 citations across multiple publications. In the SLAM domain, his SegMap framework introduced a powerful 3D segment-based map representation using learned descriptors (163 citations), while his multi-robot LiDAR SLAM system advanced search-and-rescue robotics (144 citations). Nieto has also made notable strides in agricultural robotics, developing large-scale weed mapping systems using multispectral imaging and deep learning (287 citations) and contributing to integrated aerial-ground robotic platforms for precision farming (129 citations). With thousands of citations across his body of work, his research continues to shape the future of intelligent, autonomous robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots

   426 citations · 2017
2. 2
   An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

   297 citations · 2020
3. 3
   WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

   287 citations · 2018
4. 4
   Continuous-time trajectory optimization for online UAV replanning

   247 citations · 2016
5. 5
   Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations

   198 citations · 2018
6. 6
   SegMap: 3D Segment Mapping using Data-Driven Descriptors

   163 citations · 2018
7. 7
   An informative path planning framework for UAV-based terrain monitoring

   161 citations · 2020
8. 8
   An online multi-robot SLAM system for 3D LiDARs

   144 citations · 2017
9. 9
   Continuous-time trajectory optimization for online UAV replanning

   131 citations · 2016
10. 10
    Building an Aerial–Ground Robotics System for Precision Farming: An Adaptable Solution

    129 citations · 2020

Key Collaborators

Contact & Links