1. Map for Easy Paths GIANLUCA BARDARO
a collaborative project for accessibility mapping thorough mobile data fusion
GIANLUCA BARDARO
DAVIDE A. CUCCI
ANDREA ROMANONI
MATTEO MATTEUCCI
SARA COMAI

2. What is MEP?
A multidisciplinary project from Politecnico di Milano, funded under the Polisocial program. MEP aims at developing a set of tools to enrich a public map with information about accessibility of city routes through the contribution and participation of the users.
Gianluca Bardaro - Politecnico di Milano
17/09/2018

3. How MEP works Collects data about traversable paths
Use the application
Spot obstacles and provide their location using pictures
Gianluca Bardaro - Politecnico di Milano
17/09/2018

4. Our contribution
Gianluca Bardaro - Politecnico di Milano
17/09/2018

5. Working with smartphones
Gianluca Bardaro - Politecnico di Milano
17/09/2018

6. Working with smartphones: the Good
Most of people has one
Perfect for a collaborative project
Various sensors available:
Camera
GPS
Gyroscope
Accelerometer
Magnetometer …
Gianluca Bardaro - Politecnico di Milano
17/09/2018

7. Working with smartphones: the Bad
Cheap sensors
Low accuracy/precision
Problems with timestamps
Low frequencies:
Camera (1 Hz)
GPS (1 Hz)
IMU (~120 Hz)
Gianluca Bardaro - Politecnico di Milano
17/09/2018

8. Working with Smartphones: the Ugly
Have to deal with Android Impossible to obtain raw measurements Different sensors with different characteristics Cannot calibrate sensors beforehand
Gianluca Bardaro - Politecnico di Milano
17/09/2018

9. Our solution Created for robot navigation
Framework for multi-sensor fusion
Sensors as abstract logical models
Least squares optimization
Parameter
self-calibration
Gianluca Bardaro - Politecnico di Milano
17/09/2018

10. Process synchronization
Operating System
Sensor models
Don’t deal directly with hardware sensors
Sensors are abstracted by the type of information they provide
Each sensor type is characterized by a parameteric error model
Sensor models provided for common measurement domains
Forward kinematics (e.g., diff. drive, Ackermann, omnidirectional)
Absolute position (e.g., GPS)
Linear and/or Angular Velocity (e.g., gyros, visual odometry)
Vector Field (e.g., magnetometer)
Landmark position/bearing (e.g., image features, fiducial markers)
Linear acceleration
Gianluca Bardaro - Politecnico di Milano
17/09/2018
© 2005 William Fornaciari

11. Graph Optimization
A factor graph encodes the joint probability distribution of the residuals given the current estimate of user poses and calibration parameters Nodes: poses and parameters Factors: measurement error models Max-Likelihood estimation: Γ 𝑅 𝑊 𝑡 1 , …, Γ 𝑅 𝑊 𝑡 𝑛 , Γ 𝐼𝑀𝑈 𝑅 ,𝑨,𝒃 =argmin 𝑖 𝑒𝑟𝑟 𝑖 𝑇 Ω 𝑖 𝑒𝑟𝑟 𝑖
Gianluca Bardaro - Politecnico di Milano
17/09/2018

12. Process synchronization
Operating System
Let’s build a graph
ba, b𝜔
IMUt1
IMUt2
IMUt3
IMUt4
IMUt5 x0 x1 x2 x3 x4 x5
mt1
mt2
mt3
mt4
mt5
GPSt2
GPSt4 F1 K F2
Gianluca Bardaro - Politecnico di Milano
17/09/2018
© 2005 William Fornaciari

13. IMU preintegration
IMUs work at higher frequency w.r.t other sensors (100 Hz vs 1 Hz)
Usually, a pose is kept for each IMU reading
IMU readings can be pre-integrated before estimation
𝑧 𝐼𝑀𝑈 𝑡 𝑑𝑡
𝑧 𝐺𝑃𝑆 𝑡
Γ 𝑂 𝑊 𝑡
𝑧 𝐼𝑀𝑈 𝑡 𝑡
Gianluca Bardaro - Politecnico di Milano
17/09/2018

14. Experimental results Application developed to collect sensor data
Camera
Accelerometer & gyroscope
Magnetometer
GPS
Effort to achieve maximum sensor frequency
Collected by a walking person
Gianluca Bardaro - Politecnico di Milano
17/09/2018

15. How bad are those data? Gianluca Bardaro - Politecnico di Milano
17/09/2018

16. The results
Gianluca Bardaro - Politecnico di Milano
17/09/2018

17. Robustness
Gianluca Bardaro - Politecnico di Milano
17/09/2018

18. Ongoing work & Future developments
Up to now image features are not used for trajectory determination
Integrate a system of tiepoints extraction
Deal with the lack of camera calibration
Deal with low quality images
Trade off between resolution and memory footprint
Low quality sensor
Blurred images
Gianluca Bardaro - Politecnico di Milano
17/09/2018

19. Conclusions
The true strength of the system is in the ability to deal with heterogeneous data:
different types of mobile phones, with different types of sensors;
different quality of measurements.
Are the resulting trajectory satisfactory? Yes and no.
… but MEP is a collaborative project:
we can use multiple results together to enhance the final trajectory;
we can ignore a trajectory if it provides no useful information;
we can “snap” the estimated trajectory to a map.
Gianluca Bardaro - Politecnico di Milano
17/09/2018

20. THANK YOU FOR YOU ATTENTION QUESTIONS?
Gianluca Bardaro - Politecnico di Milano
17/09/2018