Search Results for author:
Found 5 papers, 3 papers with code
Uncertainty-Aware DNN for Multi-Modal Camera Localization
In the literature, uncertainty estimation in Deep Neural Networks (DNNs) is often performed through sampling methods, such as Monte Carlo Dropout (MCD) and Deep Ensemble (DE), at the expense of undesirable execution time or an increase in hardware resources.
LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM
Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time.
A Benchmark for Point Clouds Registration Algorithms
In this way, we are able to cover many kinds of environment and many kinds of sensor that can produce point clouds.
Global visual localization in LiDAR-maps through shared 2D-3D embedding space
In this work we leverage Deep Neural Network (DNN) approaches to create a shared embedding space between images and LiDAR-maps, allowing for image to 3D-LiDAR place recognition.
CMRNet: Camera to LiDAR-Map Registration
In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data.
