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Found 6 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.
CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps
In this paper, we now take it a step further by introducing CMRNet++, which is a significantly more robust model that not only generalizes to new places effectively, but is also independent of the camera parameters.
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.
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relying only on a noisy line detector and tracker.
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.
