132 citations · 177 across the 13 of their papers we have counts for
6 papers · 1 filter
Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Pedro P. B. de Gusmao +3
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment. However, the performance of such systems highly de…
SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation
Yasin Almalioglu, Mehmet Turan, Alp Eren Sari +4
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 estimat…
DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
Wei Wang, Muhamad Risqi U. Saputra, Peijun Zhao +5
Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have br…
Distilling Knowledge From a Deep Pose Regressor Network
Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Yasin Almalioglu +2
This paper presents a novel method to distill knowledge from a deep pose regressor network for efficient Visual Odometry (VO). Standard distillation relies on "dark knowledge" for…
Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning
Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Sen Wang +2
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 this paper, we stud…
Fast Training of Convolutional Neural Networks via Kernel Rescaling
Pedro Porto Buarque de Gusmão, Gianluca Francini, Skjalg Lepsøy +1
Training deep Convolutional Neural Networks (CNN) is a time consuming task that may take weeks to complete. In this article we propose a novel, theoretically founded method for red…