10 citations · 10 across the 16 of their papers we have counts for
7 papers · 1 filter
Single-Shot Metric Depth from Focused Plenoptic Cameras
Blanca Lasheras-Hernandez, Klaus H. Strobl, Sergio Izquierdo +3
Metric depth estimation from visual sensors is crucial for robots to perceive, navigate, and interact with their environment. Traditional range imaging setups, such as stereo or st…
Alignment Scores: Robust Metrics for Multiview Pose Accuracy Evaluation
Seong Hun Lee, Javier Civera
We propose three novel metrics for evaluating the accuracy of a set of estimated camera poses given the ground truth: Translation Alignment Score (TAS), Rotation Alignment Score (R…
Tightly Coupled SLAM with Imprecise Architectural Plans
Muhammad Shaheer, Jose Andres Millan-Romera, Hriday Bavle +4
Robots navigating indoor environments often have access to architectural plans, which can serve as prior knowledge to enhance their localization and mapping capabilities. While som…
Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition
Sergio Izquierdo, Javier Civera
Visual Place Recognition (VPR) plays a critical role in many localization and mapping pipelines. It consists of retrieving the closest sample to a query image, in a certain embeddi…
Camera Motion Estimation from RGB-D-Inertial Scene Flow
Samuel Cerezo, Javier Civera
In this paper, we introduce a novel formulation for camera motion estimation that integrates RGB-D images and inertial data through scene flow. Our goal is to accurately estimate t…
Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments
Alberto García-Hernández, Riccardo Giubilato, Klaus H. Strobl +2
Perceptual aliasing and weak textures pose significant challenges to the task of place recognition, hindering the performance of Simultaneous Localization and Mapping (SLAM) system…
PCR-99: A Practical Method for Point Cloud Registration with 99 Percent Outliers
Seong Hun Lee, Javier Civera, Patrick Vandewalle
We propose a robust method for point cloud registration that can handle both unknown scales and extreme outlier ratios. Our method, dubbed PCR-99, uses a deterministic 3-point samp…