2 citations · 5 across the 17 of their papers we have counts for
18 papers
Semantic Landmark Particle Filter for Robot Localisation in Vineyards
Rajitha de Silva, Jonathan Cox, James R. Heselden +3
Reliable localisation in vineyards is hindered by row-level perceptual aliasing: parallel crop rows produce nearly identical LiDAR observations, causing geometry-only and vision-ba…
Perception-Aware Autonomous Exploration in Feature-Limited Environments
Moji Shi, Rajitha de Silva, Hang Yu +2
Autonomous exploration in unknown environments typically relies on onboard state estimation for localisation and mapping. Existing exploration methods primarily maximise coverage e…
EAAE: Energy-Aware Autonomous Exploration for UAVs in Unknown 3D Environments
Jacob Elskamp, Moji Shi, Leonard Bauersfeld +2
Battery-powered multirotor unmanned aerial vehicles (UAVs) can rapidly map unknown environments, but mission performance is often limited by energy rather than geometry alone. Stan…
Active Informative Planning for UAV-based Weed Mapping using Discrete Gaussian Process Representations
Jacob Swindell, Marija Popović, Riccardo Polvara
Accurate agricultural weed mapping using unmanned aerial vehicles (UAVs) is crucial for precision farming. While traditional methods rely on rigid, pre-defined flight paths and int…
Dynamic Visual SLAM using a General 3D Prior
Xingguang Zhong, Liren Jin, Marija Popović +2
Reliable incremental estimation of camera poses and 3D reconstruction is key to enable various applications including robotics, interactive visualization, and augmented reality. Ho…
Uncertainty-Informed Active Perception for Open Vocabulary Object Goal Navigation
Utkarsh Bajpai, Julius Rückin, Cyrill Stachniss +1
Mobile robots exploring indoor environments increasingly rely on vision-language models to perceive high-level semantic cues in camera images, such as object categories. Such model…
Globally Consistent RGB-D SLAM with 2D Gaussian Splatting
Xingguang Zhong, Yue Pan, Liren Jin +3
Recently, 3D Gaussian splatting-based RGB-D SLAM displays remarkable performance of high-fidelity 3D reconstruction. However, the lack of depth rendering consistency and efficient…
DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction
Sicong Pan, Liren Jin, Xuying Huang +3
Active object reconstruction is crucial for many robotic applications. A key aspect in these scenarios is generating object-specific view configurations to obtain informative measu…
PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map
Yue Pan, Xingguang Zhong, Liren Jin +4
Robots benefit from high-fidelity reconstructions of their environment, which should be geometrically accurate and photorealistic to support downstream tasks. While this can be ach…
ActiveGS: Active Scene Reconstruction Using Gaussian Splatting
Liren Jin, Xingguang Zhong, Yue Pan +3
Robotics applications often rely on scene reconstructions to enable downstream tasks. In this work, we tackle the challenge of actively building an accurate map of an unknown scene…
Active Learning of Robot Vision Using Adaptive Path Planning
Julius Rückin, Federico Magistri, Cyrill Stachniss +1
Robots need robust and flexible vision systems to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonom…
Towards Map-Agnostic Policies for Adaptive Informative Path Planning
Julius Rückin, David Morilla-Cabello, Cyrill Stachniss +2
Robots are frequently tasked to gather relevant sensor data in unknown terrains. A key challenge for classical path planning algorithms used for autonomous information gathering is…
STAIR: Semantic-Targeted Active Implicit Reconstruction
Liren Jin, Haofei Kuang, Yue Pan +2
Many autonomous robotic applications require object-level understanding when deployed. Actively reconstructing objects of interest, i.e. objects with specific semantic meanings, is…
Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning
Sicong Pan, Liren Jin, Xuying Huang +3
Object reconstruction is relevant for many autonomous robotic tasks that require interaction with the environment. A key challenge in such scenarios is planning view configurations…
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
Apoorva Vashisth, Julius Rückin, Federico Magistri +2
Autonomous robots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially…
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
Julius Rückin, Federico Magistri, Cyrill Stachniss +1
Semantic segmentation enables robots to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots…
Active Implicit Reconstruction Using One-Shot View Planning
Hao Hu, Sicong Pan, Liren Jin +2
Active object reconstruction using autonomous robots is gaining great interest. A primary goal in this task is to maximize the information of the object to be reconstructed, given…
How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF?
Sicong Pan, Liren Jin, Hao Hu +2
Neural Radiance Fields (NeRFs) are gaining significant interest for online active object reconstruction due to their exceptional memory efficiency and requirement for only posed RG…