Transcript Accuracy Evaluation of Stereo Vision Aided Inertial

Accuracy Evaluation of Stereo Vision Aided Inertial
Navigation for Indoor Environments
D. Grießbach, D. Baumbach, A. Börner, S. Zuev
German Aerospace Center (DLR), Institute of Robotics and Mechatronics,
Dept. of Data Processing for Optical Systems, Rutherfordstr. 2, Berlin
Overview
- Introduction
- Integrated Positioning System
- Inertial Navigation
- Stereo Vision
- Real time Framework
- Experimental Results
- Conclusions and Outlook
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Motivation
- Navigation
- Determination of 6 DoF (Position, Attitude)
- Path planning, collision avoidance, …
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Introduction
- Assumption:
- No single technology is able to provide accurate position and attitude
- Multi-sensor approach is needed
- DLR research aims to:
- Generic developments
- Indoor/Outdoor capability
- No infrastructure/external referencing
- No maps (unknown environments)
- No a priori assumptions
- Passive system
- Real time
Fusion
6𝐷𝑜𝐹 → (𝑥, 𝑦, 𝑧, 𝛼, 𝛽, 𝛾)
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Inertial Navigation - Inertial Measurement Unit
- Technology: inertial navigation, based on dead reckoning
- Core component: Inertial Measurement Unit (IMU)
- Angular velocity [deg/s]
- Accelerations [m/s2]
- Microelectromechanical System IMU (MEMS)
- Small
- Lightweight
- Low cost
- Low Energy consumption
- Very robust
- Already widely used
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Inertial Navigation - Mechanization
- Strapdown mechanization for IMU integration
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Inertial Navigation - Mechanization
- Strapdown mechanization for IMU integration
- Integration
- Bias  Drift
- Noise  Random Walk
- MEMS-IMUs aggravate the problem
- High noise
- Less stable bias
- Less stable scale factors
- g-dependent errors
- Aiding systems are necessary!
- GPS/Galileo
- Camera (stereo vision)
- …
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Stereo Vision – Visual Odometry
𝑀𝑘
-
Extraction of natural feature points
-
Matching using epipolar geometry to
constrain the correspondence problem
-
-
𝑚𝑘
𝑚′𝑘
Triangulating image points 𝑚𝑘 and 𝑚′𝑘
to get object point 𝑀𝑘
𝑡𝑘
∆𝑇
Mapping of an object point: 𝑚𝑘 = 𝑃 ∙ 𝑀𝑘
𝑡𝑘+1
-
Prediction: 𝑚𝑘+1 = 𝑃 ∙ ∆𝑇 ∙ 𝑀𝑘
-
Matching and pose estimation with: min 𝑚𝑘+1 − 𝑃 ∙ ∆𝑇 ∙ 𝑀𝑘
2
∆𝑇
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Real Time Data Handling
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Experimental Results – Setup
- Visual aided navigation with:
- Stereo camera system (15 Hz)
- Low cost MEMS based IMU (400 Hz)
- Low cost MEMS based inclinometer
- Sigma Point Kalman filter for sensor fusion
- “Given” prerequisites
- Synchronized measurements
- Calibrated cameras
- Calibrated IMU
- Registered sensors
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Experimental Results – Navigation
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Experimental Results – Navigation
-
Unknown indoor environment
Path of about 317 m
Covering 4 floors
Closed loop for evaluation
21 similar runs
- Tracker uses about 60 Features
- Frame to frame accuracy:
5 mm/0.2 deg (2 mm/0.1 deg for
viewing axis)
- Final distance error < 1% of total
path length
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Experimental Results – Closed Loop Error
Mean
1σ Std
𝛼 [deg]
-0.1
0.2
𝛽 [deg]
-0.1
0.3
𝛾 [deg]
5.5
14.5
x [m]
0.4
1.0
y [m]
0.5
2.2
z [m]
-0.2
1.7
absolute [m]
2.7
1.3
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Experimental Results – 3D Point Cloud
- Parallel processing chain with matching with Semi Global Matching (SGM)
- Combined with navigation solution to generate 3D point cloud
- not real time yet
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Conclusion
- A framework for multi-sensor navigation was presented
- The framework was applied for visual aided inertial navigation with
low cost MEMS-components and stereo vision
- Accuracy of < 1% of the total path length (over 21 runs)
- Low textured scenes cause short periods of pure inertial navigation
- Uncompensated IMU errors (scale error, g-sensitivity, …)
- Additional processing of a high density 3D point cloud
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown
Outlook
- Creation of reference data sets (Camera, IMU, GPS, etc.) with ground truth
measurements for indoor and outdoor environments
- Synchronized
- Calibrated
- Registered
- Advancing the modelling and calibration of low cost IMUs
- Integrating absolute position measurements
- GNSS measurements for outdoor
- RFID/Wi-Fi/Bluetooth measurements for indoor
- Seamless outdoor/indoor navigation
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments > Denis Grießbach > ISPRS 2013 > Capetown