GPS-Denied Geo-Localisation Using Visual Odometry

Gupta, Ashish; Chang, Huan; Yilmaz, Alper

doi:10.5194/isprs-annals-iii-3-263-2016

Cited by 8 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common form of localization problem is to use sensory readings to estimate the absolute coordinates of the object on the map using Bayesian filtering [33][34][35][36][37]. The authors of [33] presented a Bayesian approach to model the posterior distribution of the position given the prior map, which is considered a classic method commonly adopted in the robotics field.…”

Section: Probabilistic Localizationmentioning

confidence: 99%

Subgraph Learning for Topological Geolocalization with Graph Neural Networks

Zha

Yilmaz

2023

Sensors

View full text Add to dashboard Cite

One of the challenges of spatial cognition, such as self-localization and navigation, is to develop an efficient learning approach capable of mimicking human ability. This paper proposes a novel approach for topological geolocalization on the map using motion trajectory and graph neural networks. Specifically, our learning method learns an embedding of the motion trajectory encoded as a path subgraph where the node and edge represent turning direction and relative distance information by training a graph neural network. We formulate the subgraph learning as a multi-class classification problem in which the output node IDs are interpreted as the object’s location on the map. After training using three map datasets with small, medium, and large sizes, the node localization tests on simulated trajectories generated from the map show 93.61%, 95.33%, and 87.50% accuracy, respectively. We also demonstrate similar accuracy for our approach on actual trajectories generated by visual-inertial odometry. The key benefits of our approach are as follows: (1) we take advantage of the powerful graph-modeling ability of neural graph networks, (2) it only requires a map in the form of a 2D graph, and (3) it only requires an affordable sensor that generates relative motion trajectory.

show abstract

Section: Probabilistic Localizationmentioning

confidence: 99%

Subgraph Learning for Topological Geolocalization with Graph Neural Networks

Zha

Yilmaz

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Zhang and Singh (2015) developed a method using a laser scanner/camera combination which initialises the position estimate with VO, but refines the platform's motion utilising the laser scanner for scan matching. Gupta et al (2016) combine digital map information with VO to localise a vehicle without any GNSS positioning, and achieve metre-range accuracy. VO is a technology of significant importance for autonomous driving and many different approaches are compared using the KITTI dataset (Geiger et al, 2013).…”

Section: Alternative Strategiesmentioning

confidence: 99%

A fully automatic approach to register mobile mapping and airborne imagery to support the correction of platform trajectories in GNSS-denied urban areas

Jende¹,

Nex²,

Gerke

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

“…Previous researches integrated VO with INS [ 35 ], and/or magnetometers [ 34 ], or fused with motion constraints to aid INS [ 16 ]. VO can be integrated with maps, for example; [ 36 ] proposed a geo-localization land vehicle system based on visual odometry and public domain map sources for GNSS denied environment. A similar land vehicle navigation system for the urban environment was proposed by [ 37 ], where stereo VO using weighted non-linear estimation is implemented and fused with digital maps in which probabilistic map matching is used to limit the VO errors.…”

Section: Introductionmentioning

confidence: 99%

Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment

Moussa

Zahran

Mostafa

et al. 2020

Sensors

View full text Add to dashboard Cite

Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This proposed system is based on optical flow and multiple mass flow sensors integrations to aid the low-cost INS by providing the navigation extended Kalman filter (EKF) with forward velocity and change of heading updates to enhance the vehicle navigation. The optical flow is computed for frames taken using a consumer portable device (CPD) camera mounted in the upward-looking direction to avoid moving objects in front of the camera and to exploit the typical features of the underground parking or tunnels such as ducts and pipes. On the other hand, the multiple mass flow sensors measurements are modeled to provide forward velocity information. Moreover, a mass flow differential odometry is proposed where the vehicle change of heading is estimated from the multiple mass flow sensors measurements. This integrated aiding system can be used for unmanned aerial vehicles (UAV) and land vehicle navigations. However, the experimental results are implemented for land vehicles through the integration of CPD with mass flow sensors to aid the navigation system.

show abstract

GPS-Denied Geo-Localisation Using Visual Odometry

Cited by 8 publications

References 11 publications

Subgraph Learning for Topological Geolocalization with Graph Neural Networks

Subgraph Learning for Topological Geolocalization with Graph Neural Networks

A fully automatic approach to register mobile mapping and airborne imagery to support the correction of platform trajectories in GNSS-denied urban areas

Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment

Contact Info

Product

Resources

About