7 citations · 14 across the 5 of their papers we have counts for
5 papers · 1 filter
TACO: Trash Annotations in Context for Litter Detection
Pedro F Proença, Pedro Simões
TACO is an open image dataset for litter detection and segmentation, which is growing through crowdsourcing. Firstly, this paper describes this dataset and the tools developed to s…
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Pedro F. Proenca, Yang Gao
On-orbit proximity operations in space rendezvous, docking and debris removal require precise and robust 6D pose estimation under a wide range of lighting conditions and against hi…
Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry
Pedro F. Proença, Yang Gao
This paper presents CAPE, a method to extract planes and cylinder segments from organized point clouds, which processes 640x480 depth images on a single CPU core at an average of 3…
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
Pedro F. Proenca, Yang Gao
This work proposes a robust visual odometry method for structured environments that combines point features with line and plane segments, extracted through an RGB-D camera. Noisy d…
Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry
Pedro F. Proença, Yang Gao
This work proposes a visual odometry method that combines points and plane primitives, extracted from a noisy depth camera. Depth measurement uncertainty is modelled and propagated…