Publications

2025

1. RA-L

   

   P-AgNav: Range View-Based Autonomous Navigation System for Cornfields

   Kitae Kim, Aarya Deb, and David J. Cappelleri

   IEEE Robotics and Automation Letters, 2025

   DOI

2024

1. RA-L

   

   P-AgSLAM: In-Row and Under-Canopy SLAM for Agricultural Monitoring in Cornfields

   Kitae Kim, Aarya Deb, and David J. Cappelleri

   IEEE Robotics and Automation Letters, 2024

   DOI

2023

1. IROS

   

   Deep Learning-Based Leaf Detection for Robotic Physical Sampling with P-AgBot

   Aarya Deb, Kitae Kim, and David J. Cappelleri

   In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

   DOI
2. Sensors

   

   Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul

   Minwoo Cho, Kitae Kim, Soohyun Cho, and 2 more authors

   Sensors, 2023

   Abs DOI

   Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated.

2022

1. RA-L

   

   P-AgBot: In-Row & Under-Canopy Agricultural Robot for Monitoring and Physical Sampling

   Kitae Kim, Aarya Deb, and David J. Cappelleri

   IEEE Robotics and Automation Letters, 2022

   DOI

2021

1. RA-L

   

   HD Map Update for Autonomous Driving With Crowdsourced Data

   Kitae Kim, Soohyun Cho, and Woojin Chung

   IEEE Robotics and Automation Letters, 2021

   DOI