27 citations · 67 across the 22 of their papers we have counts for
25 papers
FoundationVIO: Enforcing Metric Scale in Feed-forward Visual-Inertial Odometry
Jin Rhee, Yuhao Zhang, Frank Dellaert +1
Feed-forward geometric foundation models (GFMs) directly predict dense geometry and camera poses from batches of images. Emerging visual SLAM methods chain together consecutive GFM…
OASIS-Map: Object-Level Change Detection in Multi-Session Mapping using Semantic Correspondence Matching
Haedam Oh, Yifu Tao, Nived Chebrolu +1
Map representations which are consistent across repeated visits to a real-world semi-static environment are very useful for long-term robotic inspection. In such settings, the scen…
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…
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…
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…
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…
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…
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…
GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation
Turcan Tuna, Jonas Frey, Frank Fu +5
Accurate state estimation and multi-modal perception are prerequisites for autonomous legged robots in complex, large-scale environments. To date, no large-scale public legged-robo…
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…
Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
Matías 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…
Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset
Zirui Wang, Wenjing Bian, Xinghui Li +4
We introduce Oxford Day-and-Night, a large-scale, egocentric dataset for novel view synthesis (NVS) and visual relocalisation under challenging lighting conditions. Existing datase…
SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification
Yifu Tao, Maurice Fallon
We present a neural radiance field (NeRF) based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically a…
The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs
Christina Kassab, Matías Mattamala, Sacha Morin +4
3D open-vocabulary scene graph methods are a promising map representation for embodied agents, however many current approaches are computationally expensive. In this paper, we reex…
The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods
Yifu Tao, Miguel Ángel Muñoz-Bañón, Lintong Zhang +3
This paper introduces a large-scale multi-modal dataset captured in and around well-known landmarks in Oxford using a custom-built multi-sensor perception unit as well as a millime…
Markerless Aerial-Terrestrial Co-Registration of Forest Point Clouds using a Deformable Pose Graph
Benoit Casseau, Nived Chebrolu, Matias Mattamala +2
For biodiversity and forestry applications, end-users desire maps of forests that are fully detailed, from the forest floor to the canopy. Terrestrial laser scanning and aerial las…
Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations
Lintong Zhang, Yifu Tao, Jiarong Lin +2
Recent advances in mapping techniques have enabled the creation of highly accurate dense 3D maps during robotic missions, such as point clouds, meshes, or NeRF-based representation…
Autonomous Forest Inventory with Legged Robots: System Design and Field Deployment
Matías Mattamala, Nived Chebrolu, Benoit Casseau +5
We present a solution for autonomous forest inventory with a legged robotic platform. Compared to their wheeled and aerial counterparts, legged platforms offer an attractive balanc…
Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision
Matías Mattamala, Jonas Frey, Piotr Libera +5
Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In…
SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection
Yifu Tao, Yash Bhalgat, Lanke Frank Tarimo Fu +3
We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capt…
LiSTA: Geometric Object-Based Change Detection in Cluttered Environments
Joseph Rowell, Lintong Zhang, Maurice Fallon
We present LiSTA (LiDAR Spatio-Temporal Analysis), a system to detect probabilistic object-level change over time using multi-mission SLAM. Many applications require such a system,…
Online Tree Reconstruction and Forest Inventory on a Mobile Robotic System
Leonard Freißmuth, Matias Mattamala, Nived Chebrolu +3
Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requi…
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…
Exosense: A Vision-Based Scene Understanding System For Exoskeletons
Jianeng Wang, Matias Mattamala, Christina Kassab +5
Self-balancing exoskeletons are a key enabling technology for individuals with mobility impairments. While the current challenges focus on human-compliant hardware and control, unl…