activity
20172026
most citedDeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

962 citations · 1.1k across the 7 of their papers we have counts for

collaborators

8 papers

cs.SD2026

Geometry-Informed Distributed Acoustic Scene Understanding

Yiyuan Yang, Shitong Xu, Niki Trigoni +1

Acoustic scene understanding in multi-room environments is a difficult task. Most existing systems use a single centralized microphone array, and they often fail because walls and…

eess.SP2019★ 4 cited

FootSLAM meets Adaptive Thresholding

Johan Wahlstrom, Andrew Markham, Niki Trigoni

Calibration of the zero-velocity detection threshold is an essential prerequisite for zero-velocity-aided inertial navigation. However, the literature is lacking a self-contained c…

eess.SP2019

Sensor Fusion for Magneto-Inductive Navigation

Johan Wahlström, Manon Kok, Pedro Porto Buarque de Gusmao +3

Magneto-inductive navigation is an inexpensive and easily deployable solution to many of today's navigation problems. By utilizing very low frequency magnetic fields, magneto-induc…

cs.CV2019★ 2 cited

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…

cs.LG2019

WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding

Shuyu Lin, Ronald Clark, Robert Birke +2

Variational Auto-encoders (VAEs) have been very successful as methods for forming compressed latent representations of complex, often high-dimensional, data. In this paper, we deri…

eess.SP2019

Zero-Velocity Detection - A Bayesian Approach to Adaptive Thresholding

Johan Wahlström, Isaac Skog, Fredrik Gustafsson +2

A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector extends existing zero-velocity detectors based on the likelihood-ratio tes…