As a result, it learns to generate minimal image perturbations that are still capable of perplexing the network.
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment.
In this demonstration, we present a real-time indoor positioning system which fuses millimetre-wave (mmWave) radar and IMU data via deep sensor fusion.
We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality.
In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimation.
There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images.
no code implementations • 16 Sep 2019 • Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Chris Xiaoxuan Lu, Yasin Almalioglu, Stefano Rosa, Changhao Chen, Johan Wahlström, Wei Wang, Andrew Markham, Niki Trigoni
The hallucination network is taught to predict fake visual features from thermal images by using Huber loss.
To the best of our knowledge, this is the first work which successfully distill the knowledge from a deep pose regression network.
Inspired by the cognitive process of humans and animals, Curriculum Learning (CL) trains a model by gradually increasing the difficulty of the training data.
In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled data is not abundant.