works on

From the 1 of 12 linked papers with an AI index.

activity
20242026
collaborators

12 papers

cs.RO2026

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…

cs.RO2026

Hilti-Trimble-Oxford Dataset: 360 Visual-Inertial Benchmark with Floor Plan Priors for SLAM and Localization

Samuele Centanni, Yuhao Zhang, Yifu Tao +8

Automated progress monitoring on construction sites is an active area of research and development. Robot and human-carried mapping systems have been developed to build 3D maps of b…

cs.RO2026

ScaRF-SLAM: Scale-Consistent Reconstruction with Feed-Forward Models and Classical Visual SLAM

Yuhao Zhang, Yifu Tao, Frank Dellaert +1

Recent works have explored unifying SLAM with geometric foundation models (GFMs). However, directly using GFM predictions for tracking is highly sensitive to model capability and u…

cs.RO2026

LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields

Dongjae Lee, Wooseong Yang, Yifu Tao +2

Neural distance fields offer a compact and continuous representation of 3D geometry, making them attractive for incremental LiDAR mapping. However, their online optimization is vul…

cs.RO2026

Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring

Miguel Ángel Muñoz-Bañón, Nived Chebrolu, Sruthi M. Krishna Moorthy +4

Saplings are key indicators of forest regeneration and overall forest health. However, their fine-scale architectural traits are difficult to capture with existing 3D sensing metho…

cs.RO2025

PlanarMesh: Building Compact 3D Meshes from LiDAR using Incremental Adaptive Resolution Reconstruction

Jiahao Wang, Nived Chebrolu, Yifu Tao +3

Building an online 3D LiDAR mapping system that produces a detailed surface reconstruction while remaining computationally efficient is a challenging task. In this paper, we presen…