3 citations · 3 across the 1 of their papers we have counts for
5 papers
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…
DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Chris Xiaoxuan Lu +7
Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fa…
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…