Blog
The latest from Google Research
Using Global Localization to Improve Navigation
Monday, February 11, 2019
Posted by Tilman Reinhardt, Software Engineer, Google Maps
One of the consistent challenges when navigating with Google Maps is figuring out the right direction to go: sure, the app tells you to go north - but many times you're left wondering, "
Where exactly am I, and
which way is north?"
Over the years, we've attempted to improve the accuracy of the blue dot with tools like GPS and compass, but found that both have physical limitations that make solving this challenge difficult, especially in urban environments.
We're experimenting with a way to solve this problem using a technique we call
global localization
, which combines
Visual Positioning Service
(VPS),
Street View
, and machine learning to more accurately identify position and orientation. Using the smartphone camera as a sensor, this technology enables a more powerful and intuitive way to help people quickly determine which way to go.
Due to limitations with accuracy and orientation, guidance via GPS alone is limited in urban environments. Using VPS, Street View and machine learning, Global Localization can provide better context on where you are relative to where you're going.
In this post, we'll discuss some of the limitations of navigation in urban environments and how global localization can help overcome them.
Where GPS Falls Short
The process of identifying the position and orientation of a device relative to some reference point is referred to as
localization
. Various techniques approach localization in different ways.
GPS
relies on measuring the delay of radio signals from multiple dedicated satellites to determine a precise location. However, in dense urban environments like New York or San Francisco, it can be incredibly hard to pinpoint a geographic location due to low visibility to the sky and signals reflecting off of buildings. This can result in highly inaccurate placements on the map, meaning that your location could appear on the wrong side of the street, or even a few blocks away.
GPS signals bouncing off facades in an urban environment.
GPS has another technical shortcoming: it can only determine the location of the device, not the orientation. Sometimes, sensors in your mobile device can remedy the situation by measuring the magnetic and gravity field of the earth and the relative motion of the device in order to give rough estimates of your orientation. But these sensors are easily skewed by magnetic objects such as cars, pipes, buildings, and even electrical wires inside the phone, resulting in errors that can be inaccurate by up to 180 degrees.
A New Approach to Localization
To improve the precision position and orientation of the blue dot on the map, a new complementary technology is necessary. When walking down the street, you orient yourself by comparing what you see with what you
expect
to see. Global localization uses a combination of techniques that enable the camera on your mobile device to orient itself much as you would.
VPS determines the location of a device based on imagery rather than GPS signals. VPS first creates a map by taking a series of images which have a known location and analyzing them for key visual features, such as the outline of buildings or bridges, to create a large scale and fast searchable index of those visual features. To localize the device, VPS compares the features in imagery from the phone to those in the VPS index. However, the accuracy of localization through VPS is greatly affected by the quality of the both the imagery and the location associated with it. And that poses another question—where does one find an extensive source of high-quality global imagery?
Enter Street View
Over 10 years ago we launched
Street View
in Google Maps in order to help people explore the world more deeply. In that time, Street View has continued to expand its coverage of the world, empowering people to not only preview their route, but also step inside famous landmarks and museums, no matter where they are. To deliver global localization with VPS, we connected it with Street View data, making use of information gathered and tested from over 93 countries across the globe. This rich dataset provides trillions of strong reference points to apply triangulation, helping more accurately determine the position of a device and guide people towards their destination.
Features matched from multiple images.
Although this approach works well in theory, making it work well in practice is a challenge. The problem is that the imagery from the phone at the time of localization may differ from what the scene looked like when the Street View imagery was collected, perhaps months earlier. For example, trees have lots of rich detail, but change as the seasons change and even as the wind blows. To get a good match, we need to filter out temporary parts of the scene and focus on permanent structure that doesn't change over time. That's why a core ingredient in this new approach is applying machine learning to automatically decide which features to pay attention to, prioritizing features that are likely to be permanent parts of the scene and ignoring things like trees, dynamic light movement, and construction that are likely transient. This is just one of the many ways in which we use machine learning to improve accuracy.
Combining Global Localization with Augmented Reality
Global localization is an additional option that users can enable when they most need accuracy. And, this increased precision has enabled the possibility of a number of new experiences. One of the newest features we're testing is the ability to use
ARCore
, Google's platform for building augmented reality experiences, to overlay directions right on top of Google Maps when someone is in walking navigation mode. With this feature, a quick glance at your phone shows you exactly which direction you need to go.
Although early results are promising, there's significant work to be done. One outstanding challenge is making this technology work everywhere, in all types of conditions—think late at night, in a snowstorm, or in torrential downpour. To make sure we're building something that's truly useful, we're starting to test this feature with select
Local Guides
, a small group of Google Maps enthusiasts around the world who we know will offer us the feedback about how this approach can be most helpful.
Like other AI-driven camera experiences such as
Google Lens
(which uses the camera to let you search what you see), we believe the ability to overlay directions over the real world environment offers an exciting and useful way to use the technology that already exists in your pocket. We look forward to continuing to develop this technology, and the potential for smartphone cameras to add new types of valuable experiences.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
materials science
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Responsible AI
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2022
May
Apr
Mar
Feb
Jan
2021
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
