advanced robotics: autonomous navigation of vehicles pandey.pdf · advanced robotics: autonomous...
TRANSCRIPT
![Page 1: Advanced Robotics: Autonomous Navigation of Vehicles pandey.pdf · Advanced Robotics: Autonomous Navigation of Vehicles Presented by: Dr. Gaurav Pandey Assistant Professor IIT Kanpur](https://reader030.vdocument.in/reader030/viewer/2022021512/5ae397487f8b9a7b218d4399/html5/thumbnails/1.jpg)
Advanced Robotics: Autonomous Navigation of Vehicles
Presented by:
Dr. Gaurav Pandey
Assistant Professor
IIT Kanpur
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Autonomous Driving: History
*Video Credit: Ford
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Autonomous Driving: Today
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*Video Credit: Ford
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Motivation: Safety & Fuel Economy
• Worldwide 1.2 million people die each year in car accidents
• In US alone 33,963 traffic deaths were reported last year.
• In India 2,00,000 traffic deaths were reported last year.
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Worldwide that’s 3287
deaths every DAY
That’s 2 deaths
every minute
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Unsafe Drivers
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Autonomous Navigation System
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Localization Planning Control
3D Map (Prior)
Obstacle Dtection/ Classification
Sensing: Lasers, Cameras, Radars
IMU: DGPS, Gyroscope, Wheel Encoders
Sensing: Lasers, Cameras, Radars
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Where am I in the map ?
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MAP Current
Observation
?
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Research Approach
X21
Xij
XN(N-1)
X0
Fusion of sensor
modalities / extrinsic
calibration of sensors
Alignment of two
instances of fused data
Place Recognition within
prior 3D map
? Generate 3D map
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Research Approach: Step 1
Fusion of sensor
modalities / extrinsic
calibration of sensors
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Velodyne Laser Scanner
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Ladybug3 Omnidirectional Camera
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Extrinsic Calibration of Perception Sensors
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Related Work
Target based Targetless
Requires special targets for
calibration
Q. Zhang & R. Pless [2004]
R. Unnikrishnan & M. Hebert [2005]
C. Mei & P. Rives [2006]
P. Nunez et. al. [2009]
G. Pandey et. al. [2010]
F. M. Mirzaei et al. [2012]
Utilizes the correlation between the
sensor data for calibration.
Bougharbal et. al. [2000]
Williams et. al. [2004]
D. Scaramuzza et. al. [2007]
G. Pandey et. al. [2012]
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Data from the Perception Sensors
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Correlation Between Sensor Measured Intensities
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Joint Histogram
Reflectivity
Gra
yscale
Mu
tual
Info
rmat
ion
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Mathematical Formulation • {Pi ; i = 1, 2, … n} = Set of 3D points
• {Xi ; i = 1, 2, … n} = Reflectivity values for these points
• {pi ; i = 1, 2, … n} = Projection of 3D points on image
pi = K[R | t] Pi
• {Yi ; i = 1, 2, … n} = Intensity values for projected points
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Image Plane
Pi, Xi
pi, Yi
[R, t]
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Mutual Information based Calibration
),(maxarg
),(log),(),(
)(log)()(
)(log)()(
),()()(),(
YXMI
yxpyxpYXH
ypypYH
xpxpXH
YXHYHXHYXMI
Yy
XYXY
Xx
Yy
YY
X
Xx
X
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•X = Reflectivity of 3D points; Y = Intensity of the pixels where 3D points are projected;
• = Extrinsic calibration parameters that allows projection of 3D points onto the image
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Sensor data fusion: Lidar and Camera
Crosswalk in front of RIC
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Research Approach: Step 2
X21
Xij
XN(N-1)
X0
Fusion of sensor
modalities / extrinsic
calibration of sensors
Alignment of two
instances of fused
data
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Scan Alignment
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Before Scan Matching After Scan Matching
Direction of Motion
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MI-based Scan Registration Algorithm
Calibration Registration
Random:
Reflectivity &
Intensity values
Random:
Extracted
Features
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MI-based Registration
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Joint Histogram of Codewords Scans to be aligned (red & blue)
-40 -30 -20 -10 0 10 20 30 Angle (degree)
Mu
tual
In
form
atio
n
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Mathematical Formulation • X = {ic
P ; i = 1, 2, … n} = Codewords extracted from scan P
• Y = {icQ ; i = 1, 2, …m} = Codewords extracted from scan Q
• {pi ; i = 1, 2, … n} = 3D points corresponding to codewords
• {qi ; i = 1, 2, … m} = 3D points corresponding to codewords
qi = R pi + t
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0 0 4 0 0 1
0 3 0 0 0 0
2 0 0 2 4 0
0 0 1 0 3 0
0 0 0 5 1 0
0 4 0 2 0 0
Codewords (P)
Codew
ord
s (Q
)
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Sparse Joint Histogram (ML Estimate)
• Typically K x K >> n
– E.g. If the vocabulary size K = 250 and the number of features extracted from the scan is ~1000
– 250*250 = 62500 >> 1000
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James Stein Estimate
• This method was proposed by Hausser & Strimmer for entropy and MI estimation and is based on shrinking the ML estimator of the distribution of a random variable Z towards a target distribution T = [T1 T2 …. TK]:
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Hausser, J., and K. Strimmer. 2009. Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. J. Mach. Learn. Res. 10:1469-1484.
0 0 .33 0
0 .25 0 0
.17 0 0 .17
0 0 .08 0
.0625 .0625 .0625 .0625
.0625 .0625 .0625 .0625
.0625 .0625 .0625 .0625
.0625 .0625 .0625 .0625
.0312 .0312 .1963 .0312
.0312 .1562 .0312 .0312
.1163 .0312 .0312 .1163
.0312 .0312 .0713 .0312
ML Estimate = n/N Prior (e.g. Uniform)
JS Estimate
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Dictionary of Codewords and Target Distribution
• We learn a dictionary of codewords (codebook) from a training dataset by hierarchical K-means clustering.
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D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 2161-2168, June 2006.
Tra
inin
g
Testi
ng
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Comparison of Cost Function
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MI cost with ML estimator MI cost with JS estimator
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Scan Alignment
Not-Aligned Aligned
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Research Approach
X21
Xij
XN(N-1)
X0
Fusion of sensor
modalities / extrinsic
calibration of sensors
Alignment of two
instances of fused data
Place Recognition within
prior 3D map
? Generate 3D map
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Prior Mapping
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Video Credit: Ford
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Prior Map
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X0 X1 X2 X3
X8 X7 X6 X5
X9 X4
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Accurate 3D Map of Environment
Constraints from scan alignment (Z)
Odometry Constraints (U)
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Prior Map Data
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Satellite Imagery Lidar Reflectivity
3D point cloud Z-height data
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Localization: Where am I in the map ?
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MAP
Current Observation
?
• GPS • Lidar Data
• Reflectivity • 3D Point cloud
• Camera Imagery
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Intensity localization
?
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Challenges: Changing Weather, Dynamic Objects,
Lighting Conditions…
Map
Current
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Voxelization of 3D world
Maximum range of Velodyne laser scanner = 100m
Number of Voxels = 200 x 200 x 50
Vertical FOV of Velodyne = 26.8 degrees
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Regularized MI Estimation
• Goods-Turing correction to account for missing words:
• Chao-Shen entropy estimate:
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Chao, A., and T. J. Shen (2003), Nonparametric estimation of Shannons index of diversity when there are unseen species in sample, Environmental and Ecological Statistics, 10(4), 429–443.
= Estimate of probability of observing a new word
= probability of observing word K
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Experiment & Results
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PR Curve: 2010 data localized on 2009 3D map
Nister, D., and H. Stewenius (2006), Scalable recognition with a vocabulary tree, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2161–2168, New York, NY, USA.
Proposed method with different features Comparison with image-only method
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Localization Results Q
uery
Scan
D
ata
base (3D
Map)
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PR Curve: 2011 data localized on 2009 3D map
Nister, D., and H. Stewenius (2006), Scalable recognition with a vocabulary tree, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2161–2168, New York, NY, USA.
Proposed method with different features Comparison with image-only method
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Localization Results Snow
Without Snow
Qu
ery
Scan
D
ata
base (3D
Map)
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Summary • In our work we exploit the statistical dependence
between the data obtained from different modalities in an information theoretic framework to enhance the robustness of algorithms required for autonomous navigation of vehicles.
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Thank You !
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