Paulo V K Borges — accepted AI-conference papers

2025

Online 6DoF Global Localisation in Forests using Semantically-Guided Re-Localisation and Cross-View Factor-Graph Optimisation

Lucas Carvalho de Lima, Ethan Griffiths, Maryam Haghighat, Simon Denman, Clinton Fookes, Paulo V. K. Borges +2

IROS 2025

This paper presents FGLoc6D, a novel approach for robust global localisation and online 6DoF pose estimation of ground robots in forest environments by leveraging deep semantically-guided re-localisation and cross-view factor graph optimisation. The proposed method addresses the challenges of aligni

Cited by 2 Source ↗Scholar ↗

2024

Learning to Simulate Tree-Branch Dynamics for Manipulation

Jayadeep Jacob, Tirthankar Bandyopadhyay, Jason Williams, Paulo V. K. Borges, Fabio Ramos

RA-L 2024

We propose to use a simulation driven inverse inference approach to model the dynamics of tree branches under manipulation. Learning branch dynamics and gaining the ability to manipulate deformable vegetation can help with occlusion-prone tasks, such as fruit picking in dense foliage, as well as mov

Cited by 9 Source ↗Scholar ↗

2024

Under-Canopy Navigation Using Aerial Lidar Maps

Lucas Carvalho de Lima, Nicholas R. J. Lawrance, Kasra Khosoussi, Paulo V. K. Borges, Michael Brünig

RA-L 2024

Autonomous navigation in unstructured natural environments poses a significant challenge. In goal navigation tasks without prior information, the limited look-ahead of onboard sensors utilised by robots compromises path efficiency. We propose a novel approach that leverages an above-the-canopy aeria

Cited by 2 Source ↗Scholar ↗

2021

Certainty Aware Global Localisation Using 3D Point Correspondences

Remo Steiner, Mark Cox, Paulo V. K. Borges, Lukas Bernreiter, Juan I. Nieto

RA-L 2021

We propose a probabilistic framework for multi-modal global localisation using 3D point correspondences without needing to integrate over SE(3) for Bayesian inference. A finite set of transformation candidates is constructed by decomposing the known global map into local places and computing the max

Cited by 2 Source ↗Scholar ↗

2020

Identification of Effective Motion Primitives for Ground Vehicles

Tobias Löw, Tirthankar Bandyopadhyay, Paulo V. K. Borges

IROS 2020poster

Understanding the kinematics of a ground robot is essential for efficient navigation. Based on the kinematic model of a robot, its full motion capabilities can be represented by theoretical motion primitives. However, depending on the environment and/or human preferences, not all of those theoretica…

Cited by 8 Source ↗Scholar ↗

2019

Local Descriptor for Robust Place Recognition Using LiDAR Intensity

Jiadong Guo, Paulo V. K. Borges, Chanoh Park, Abel Gawel

RA-L 2019

Place recognition is a challenging problem in mobile robotics, especially in unstructured environments or under viewpoint and illumination changes. Most LiDAR-based methods rely on geometrical features to overcome such challenges, as generally scene geometry is invariant to these changes, but tend t

Cited by 157 Source ↗Scholar ↗

2018

Delight: An Efficient Descriptor for Global Localisation Using LiDAR Intensities

Konrad P Cop, Paulo V K Borges, Renaud Dubé

ICRA 2018poster

Place recognition is a key element of mobile robotics. It can assist with the “wake-up” and “kidnapped robot” problems, where the robot position needs to be estimated without prior information. Among the different sensors that can be used for the task (e.g., camera, GPS, LiDAR), LiDAR has the advant…

Cited by 126 Source ↗Scholar ↗

2018

Map-Aware Particle Filter for Localization

Adrian Rechy Rormero, Paulo V K. Borges, Andreas Pfrunder, Alberto Elfes

ICRA 2018poster

This work presents a method to improve vehicle localization by using the information from a prior occupancy grid to bound the possible poses. The method, named Map-Aware Particle Filter, uses a nonlinear approach to map-matching that can be integrated into a particle filter framework for localizatio…

Cited by 18 Source ↗Scholar ↗

2018

PoseMap: Lifelong, Multi-Environment 3D LiDAR Localization

Philipp Egger, Paulo V K Borges, Gavin Catt, Andreas Pfrunder, Roland Siegwart, Renaud Dubé

IROS 2018poster

Reliable long-term localization is key for robotic systems in dynamic environments. In this paper, we propose a novel approach for long-term localization using 3D LiDARs, coined PoseMap. In essence, we extract distinctive features from range measurements and bundle these into local views along with…

Cited by 62 Source ↗Scholar ↗

2017

Real-time autonomous ground vehicle navigation in heterogeneous environments using a 3D LiDAR

Andreas Pfrunder, Paulo V. K. Borges, Adrian R. Romero, Gavin Catt, Alberto Elfes

IROS 2017poster

The ability to drive autonomously in heterogeneous environments without GPS, pattern identification (e.g. road following), or artificial landmarks is key to field robotics. To address this challenge, we present a complete waypoint navigation framework for unmanned ground vehicles. A Velodyne PUCK VL…

Cited by 103 Source ↗Scholar ↗

2016

Low-obstacle detection using stereo vision

Robert Bichsel, Paulo V K Borges

IROS 2016poster

Real-time obstacle detection is a key component of autonomous vehicles. In this context, low obstacles are particularly challenging, as they are often discarded by traditional algorithms. Curb detection methods that can potentially be suitable for the problem usually target roads with clearly define…

Cited by 8 Source ↗Scholar ↗