50 citations · 97 across the 15 of their papers we have counts for
15 papers
Semantic-weighted ICP for LiDAR Odometry: Class-Aware Residual Reweighting for Robust Scan Registration
Vasco Carvalho, Tiago Barros, Urbano J. Nunes
LiDAR odometry is a fundamental component of autonomous robotic systems, relying on geometric registration between consecutive point clouds to estimate ego-motion. However, traditi…
Seg2Track++: Probabilistic Track Validation and Data Association for Multi-Object Tracking and Segmentation
Diogo Mendonça, Tiago Barros, Cristiano Premebida +1
Autonomous systems require robust Multi-Object Tracking and Segmentation (MOTS) to operate reliably in dynamic environments, ensuring consistent object identities and precise mask-…
HortiMulti: A Multi-Sensor Dataset for Localisation and Mapping in Horticultural Polytunnels
Shuoyuan Xu, Zhipeng Zhong, Tiago Barros +4
Agricultural robotics is gaining increasing relevance in both research and real-world deployment. As these systems are expected to operate autonomously in more complex tasks, the a…
Seg2Track-SAM2: SAM2-based Multi-object Tracking and Segmentation
Diogo Mendonça, Tiago Barros, Cristiano Premebida +1
Autonomous-driving perception systems require robust Multi-Object Tracking (MOT) to operate reliably in dynamic environments. MOT maintains consistent object identities across fram…
Impact of 3D LiDAR Resolution in Graph-based SLAM Approaches: A Comparative Study
J. Jorge, T. Barros, C. Premebida +3
Simultaneous Localization and Mapping (SLAM) is a key component of autonomous systems operating in environments that require a consistent map for reliable localization. SLAM has be…
PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture
T. Barros, L. Garrote, P. Conde +4
3D LiDAR-based place recognition remains largely underexplored in horticultural environments, which present unique challenges due to their semi-permeable nature to laser beams. Thi…
Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification
Ricardo Pereira, Luís Garrote, Tiago Barros +2
Indoor scenes are usually characterized by scattered objects and their relationships, which turns the indoor scene classification task into a challenging computer vision task. Desp…
Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches
Nuno Cunha, Tiago Barros, Mário Reis +3
Multispectral imagery is frequently incorporated into agricultural tasks, providing valuable support for applications such as image segmentation, crop monitoring, field robotics, a…
TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition
Tiago Barros, Luís Garrote, Martin Aleksandrov +2
Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-…
Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks
Pedro Conde, Tiago Barros, Rui L. Lopes +2
With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. Thi…
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards
T. Barros, L. Garrote, P. Conde +4
Robust and reliable place recognition and loop closure detection in agricultural environments is still an open problem. In particular, orchards are a difficult case study due to st…
A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification
Ricardo Pereira, Tiago Barros, Luis Garrote +2
This work proposes a novel approach that uses a semantic segmentation mask to obtain a 2D spatial layout of the segmentation-categories across the scene, designated by segmentation…
Multispectral Vineyard Segmentation: A Deep Learning approach
T. Barros, P. Conde, G. Gonçalves +4
Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural…
Place recognition survey: An update on deep learning approaches
Tiago Barros, Ricardo Pereira, Luís Garrote +2
Autonomous Vehicles (AV) are becoming more capable of navigating in complex environments with dynamic and changing conditions. A key component that enables these intelligent vehicl…
AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition
Tiago Barros, Luís Garrote, Ricardo Pereira +2
LiDAR-based place recognition is one of the key components of SLAM and global localization in autonomous vehicles and robotics applications. With the success of DL approaches in le…