Low Infrastructure Navigation for Indoor Environments October 31, 2012 Arne Suppé...
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Transcript of Low Infrastructure Navigation for Indoor Environments October 31, 2012 Arne Suppé...
Low Infrastructure Navigation for Indoor
EnvironmentsOctober 31, 2012
Arne Suppé [email protected]
CMU NavLab Group
Overview
We have demonstrated camera based navigation of a vehicle in a parking garage
We propose to:Work with AUDI/VW to realistically demonstrate
algorithm and collect development dataProve robustness in wide variety of real-world
environments using actual automotive sensorsExplore solutions to reduce computational/data
footprint to levels realistic for a vehicle in 5 to 10 years
Why Use Cameras for Navigation?
Cameras and computation are cheap and projected to get cheaper
3D LIDARs are large, expensive, and not likely to get cheap or rugged enough for automotive environment
High infrastructure costs to equip indoor environments with fiducials or beacons
Cameras are Not Enough
Motion sensors:Have higher update rates and better incremental
precisionHandle cases where camera-based solutions failsConstrain solutions to make camera-based
navigation more tractable
Cameras contain drift in pose estimate
AUDI/VW’s expertise can help us to collect synchronized camera and real automotive motion sensor data to develop and benchmark our algorithms
The Benefit of Combining Camera and Motion Sensors
Position Drift
Dro
p O
ut
Expensive Gyro AutomotiveGyro
Camera Navigation
Camera + Automotive Gyro ≈ Expensive IMU
Building on Our Existing Work
Tailor system to take advantage of overhead environments.Known entrance and egress points to structuresDo not need to solve lost robot problem – only require
incremental solutionWe already do this when in EKF locked inSmaller search space than outdoor problem – can
employ stronger inference techniques
Ceiling very invariant in overhead environmentsClassic result in indoor robot navigation. [Thrun 2000]
One camera instead of twoReduce physical costsReduce computational costs
Less data to process Potential to vastly simplify solution for camera motion
Alternative Camera Locations
Current Camera Locations
Alternative CameraLocation
Forward
Alternative Algorithms
Explore alternative algorithms to measure camera motion to: Reduce computational cost for position refinement Reduce V2V and V2I communications requirements to transmit
map representations Improve robustness
www.123dapp.com www.photosynth.net
The Virtual ValetPresented by Arne Suppé
With work by: Hernan Badino. Hideyuki Kume
Luis Navarro-Serment & Aaron Steinfeld
October 23, 2012
Existing Vision Based Path Tracking
OfflineBuild location tagged image database recording
reference trajectoryLocations need only be locally consistent
OnlineReplay trajectory
1. Solve global localization problem
2. Refine position estimate
3. Fuse with vehicle sensors
Building the Database
Use structure from motion to reconstruct a smooth trajectory of the camera through the environment.[Wu, 2011]
Feature points Camera poses
Global Localization
Find relevant images in the database given new image Returns location of most similar database image
Whole image SURF descriptor – weak similarity metric
Topometric mapping [Badino 2012] We know which images should be near each other We know how fast the vehicle is moving
DatabaseDatabase CurrentCurrent
Dis
tan
ce
Filter State (Database Images in Traversal Sequence)
Log Probability of Vehicle Location as it Travels
1 2 3 4 5 6 7 8 9
Position Refinement
Recover 6-DOF displacement between database and query image.Database location + displacement = current global
locationOnline process – uses GPU accelerated SIFT feature
matching and RANSAC homographyDatabaseDatabase QueryQuery
Database
Query
Sensor Data Fusion Image matching solution may be noisy, wrong, not exist
EKF fuses camera data with cheap, automotive sensors Reduces noise while vision contains drift
Estimation used for vehicle control
Direction of Travel
Matched Database Image Location
Vehicle Position Covariance
Vehicle Position Estimate
InitializationLock-OnLoss of LockLock Reacquired
Global Localization
Global Localization
Position Refinement
Position Refinement
EKF FusionEKF Fusion
Position Tagged Database
Navigation Cameras
Position Refinement
Position Refinement
EKF FusionEKF Fusion
Global Position Information
Init
ializ
ati
on
Aft
er
Lock
-On
Vehicle Platform
NavLab 11 - 2000 Jeep Wrangler Throttle, brake, and steering actuators Crossbow IMU, KVH Fiberoptic yaw gyro,
Odometry
Computing 5 Intel Core i7 M 620, 2 cores @ 2.67 GHz, 8
GB RAM Command & control, vehicle state, obstacle
detection, etc.
1 Intel Core i7-2600K, 4 cores @ 3.4 GHz, 16 GB RAM Nvidia GeForce GTX580 Fermi Structure from motion localization
Navigation Camera
Panorama Camera
Collision Warning LIDAR
References
Probablistic Algorithms and the Interactive Museum Tour-Guide Robot MINERVA, S. Thrun, M. Beetz, M. Bennewitz, W. Burgard, A.B. Cremers, F. Dellaert, D. Fox, D. Haehnel, C. Rosenberg, N. Roy, J. Schulte, D. Schulz. International Journal of Robotics Resesarch, 2000.
VisualSFM : A Visual Structure from Motion System, Changchang Wu, http://www.cs.washington.edu/homes/ccwu/vsfm/
Real-Time Topometric Localization. Hernan Badino, Daniel Huber, Takeo Kanade. International Conference on Robotics and Automation, May 2012
Semi-Autonomous Virtual Valet Parking. Arne Suppe, Luis Navarro-Serment, Aaron Steinfeld. AutomotiveUI 2010