From the 1 of 5 linked papers with an AI index.
5 papers
OASIS-Map: Object-Level Change Detection in Multi-Session Mapping using Semantic Correspondence Matching
Haedam Oh, Yifu Tao, Nived Chebrolu +1
The paper introduces OASIS-Map, a multi‑session robotic mapping system that detects object‑level changes by matching dense semantic patches across visits, enabling reliable object…
TreeLoc++: Robust 6-DoF LiDAR Localization in Forests with a Compact Digital Forest Inventory
Minwoo Jung, Dongjae Lee, Nived Chebrolu +3
Reliable localization is essential for sustainable forest management, as it allows robots to revisit and monitor the status of individual trees over long periods. In modern forestr…
TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
Minwoo Jung, Nived Chebrolu, Lucas Carvalho de Lima +3
Reliable localization is crucial for navigation in forests, where GPS is often degraded and LiDAR measurements are repetitive, occluded, and structurally complex. These conditions…
Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
MatÃas Mattamala, Nived Chebrolu, Jonas Frey +5
Legged robots are increasingly being adopted in industries such as oil, gas, mining, nuclear, and agriculture. However, new challenges exist when moving into natural, less-structur…
Evaluation and Deployment of LiDAR-based Place Recognition in Dense Forests
Haedam Oh, Nived Chebrolu, Matias Mattamala +2
Many LiDAR place recognition systems have been developed and tested specifically for urban driving scenarios. Their performance in natural environments such as forests and woodland…