Off-the-Shelf Vision-Based Mobile Robot Sensing

Zhichao Chen

Advisor: Dr. Stan Birchfield

Clemson University

Vision in Robotics

• A robot has to perceive its surroundings in order to interact with it.

• Vision is promising for several reasons:Non-contact (passive) measurementLow cost Low powerRich capturing ability

Project ObjectivesPath following: Traverse a desired trajectory in both indoor and outdoor environments.1. “Qualitative vision-based mobile robot navigation”, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2006.2. “Qualitative vision-based path following”, IEEE Transactions on Robotics, 25(3):749-754, June 2009.

Person following: Follow a person in a cluttered indoor environment.“Person Following with a Mobile Robot Using Binocular Feature-Based tracking”, Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2007

Door detection: Build a semantic map of the locations of doors as the robot drives down a corridor.“Visual detection of lintel-occluded doors from a single camera”, IEEE Computer Society Workshop on Visual Localization for Mobile Platforms (in association with CVPR),2008,

Motivation for Path Following

• Goal: Enable mobile robot to follow a desired trajectory in both indoor and outdoor environments

• Applications: courier, delivery, tour guide, scout robots

• Previous approaches:• Image Jacobian [Burschka and Hager 2001]

• Homography [Sagues and Guerrero 2005] • Homography (flat ground plane) [Liang and Pears 2002]

• Man-made environment [Guerrero and Sagues 2001]

• Calibrated camera [Atiya and Hager 1993]

• Stereo cameras [Shimizu and Sato 2000]

• Omni-directional cameras [Adorni et al. 2003]

Our Approach to Path Following

• Key intuition: Vastly overdetermined system(Dozens of feature points, one control decision)

• Key result: Simple control algorithm– Teach / replay approach using sparse feature points – Single, off-the-shelf camera– No calibration for camera or lens– Easy to implement (no homographies or Jacobians)

Preview of Results

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Tracking Feature Points

Kanade-Lucas-Tomasi (KLT) feature tracker• Automatically selects features using eigenvalues of 2x2 gradient

covariance matrix

• Automatically tracks features by minimizing sum of squared differences (SSD) between consecutive image frames

• Augmented with gain and bias to handle lighting changes

• Open-source implementation

WdJI x

dx

dx

2

22

WdJI x

dx

dx

2

)2

(2

[http://www.ces.clemson.edu/~stb/klt]

unknown displacement

gray-level images

W

TZ )()( xgxggradient of image

Teach-Replay

Teaching Phase

start

destination

detect features

trackfeatures

Replay Phase

trackfeatures

comparefeatures

current featuregoal feature

initial feature

goal feature

Qualitative Decision RuleLandmark

image plane

Feature is to the right |uCurrent| > |uGoal| “Turn right”

Feature has changed sides sign(uCurrent) ≠ sign(uGoal) “Turn left”

No evidence“Go straight”

feature

funnel lane

Robot at goal

uGoal

uCurrent

Feature is to the right “Turn right”

Side change “Turn left”

The Funnel Lane at an AngleLandmark

image plane

Robot at goal

feature

α

α α

funnel lane

No evidence“Go straight”

A Simplified Example

“Turn right” “Turn left”“Go straight”

Landmarkfeature

Robot at goal

funnel lanefunnel lanefunnel lanefunnel lane

“Go straight”“Go straight”“Go straight”

The funnel Lane Created by Multiple Feature Points

α

α

Landmark #1

Landmark #2

Landmark #3

Feature is to the right “Turn right”

Side change “Turn left”

No evidence“Do not turn”

Qualitative Control Algorithm

DC

DC

u signu sign

and

uu

Desired heading

Funnel constraints:

uGoal

otherwise 0

uu and 0(u if )u,φ(u,u max uu and 0(u if )u,φ(u,u min

θ DCCDCC

DCCDCC

id

where φ is the signed distance between the uC and uD

Incorporating Odometry

N

1io

idd β)θ(1θ

N

1βθ

Desired heading

Desired heading from odometry

Desired heading from ith feature

point

N is the number of the features;

[0,1]β

Overcoming Practical Difficulties

To deal with rough terrain:Prior to comparison, feature coordinates are warped to compensate for a non-zero roll angle about the optical axis by applying the RANSAC algorithm.

To avoid obstacles:The robot detects and avoids an obstacle by sonar, and theodometry enables the robot to roughly return to the path. Then the robot converges to the path using both odometry and vision.

Experimental Results

Videos available at http://www.ces.clemson.edu/~stb/research/mobile_robot

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Experimental Results

Videos available at http://www.ces.clemson.edu/~stb/research/mobile_robot

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Experimental Results: Rough Terrain

Experimental Results:Avoiding an Obstacle

Experimental Results

Indoor Outdoor

Imaging Source Firewire camera Logitech Pro 4000 webcam

Project ObjectivesPath following: Enable mobile robot to follow a desired trajectory in both indoor and outdoor environments.1. “Qualitative vision-based mobile robot navigation”, Proceedings of theIEEE International Conference on Robotics and Automation (ICRA), 2006.2. “Qualitative vision-based path following”, IEEE Transactions on Robotics, 2009

Person following: Enable a mobile robot to follow a person in a cluttered indoor environment by vision.“Person Following with a Mobile Robot Using Binocular Feature-Based tracking”, Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2007

Door detection: Detect doors as the robot drives down a corridor.“Visual detection of lintel-occluded doors from a single camera”, IEEE Computer Society Workshop on Visual Localization for Mobile Platforms (in association with CVPR),2008

Motivation

• Goal: Enable a mobile robot to follow a person in a cluttered

indoor environment by vision.• Previous approaches:

• Appearance properties: color, edges. [Sidenbladh et al. 1999, Tarokh and Ferrari 2003, Kwon et al. 2005]

Person has different color from background or faces camera. Lighting changes.

• Optical flow. [Piaggio et al 1998, Chivilò et al. 2004]

Drift as the person moves with out-of-plane rotation

• Dense stereo and odometry. [Beymer and Konolige 2001]

difficult to predict the movement of the robot (uneven surfaces, slippage in the wheels).

Our approach

• Algorithm: Sparse stereo based on Lucas-Kanade feature tracking.

• Handles:• Dynamic backgrounds.• Out-of-plane rotation.• Similar disparity between the person and

background.• Similar color between the person and

background.

System overview

Detect 3D features of the scene ( Cont. )

• Features are selected in the left image IL and matched in the right image IR.

Left image Right image

The size of each square indicates the horizontal disparity of the feature.

System overview

Detecting Faces

• The Viola-Jones frontal face detector is applied.• This detector is used both to initialize the system

and to enhance robustness when the person is facing the camera.

Note: The face detector is not necessary in our system.

Overview of Removing Background

2) using the estimated motion of the background.

3) using the estimated motion of the person

1) using the known disparity of the person in the previous image frame.

Discard features for whichwhere is the known disparity of the person in the previous frame,

and is the disparity of a feature at time t .

Remove BackgroundStep 1: Using the known disparity

dtt dd ||~

td~

td

Original features

Foreground features in step 1 Background features

Remove BackgroundStep 2: Using background motion

• Estimate the motion of the background by computing a 4 × 4 affine transformation matrix H between two image frames at times t and t + 1:

(1) 1

f

1

f i1t

it

H

• Random sample consensus (RANSAC) algorithm is used to yield dominant motion.

Foreground features with similar disparity in step 1

Foreground features after step 2

Remove BackgroundStep 3: Using person motion

• Similar to step 2, the motion model of the person is calculated.• The size of the person group should be the biggest.• The centroid of the person group should be proximate to

the previous location of the person.

Foreground features after step 2 Foreground features after step 3

System overview

System overview

Experimental Results

Video

Project ObjectivesPath following: Enable mobile robot to follow a desired trajectory in both indoor and outdoor environments.1. “Qualitative vision-based mobile robot navigation”, Proceedings of theIEEE International Conference on Robotics and Automation (ICRA), 2006.2. “Qualitative vision-based path following”, IEEE Transactions on Robotics, 2009

Person following: Enable a mobile robot to follow a person in a cluttered indoor environment by vision.“Person Following with a Mobile Robot Using Binocular Feature-Based tracking”, Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2007

Door detection: Detect doors as the robot drives down a corridor.“Visual detection of lintel-occluded doors from a single camera”, IEEE Computer Society Workshop on Visual Localization for Mobile Platforms (in association with CVPR),2008

Motivation for Door DetectionTopological mapMetric map

Either way, doors are semantically meaningful

landmarks

Previous Approaches to Detecting Doors

fuzzy logic [Munoz-Salinas et al. 2004]

color segmentation [Rous et al. 2005]

neural network[Cicirelli et al 2003]

Limitations: • require different colors for doors and walls• simplified environment (untextured floor, no reflections) • limited viewing angle• high computational load • assume lintel (top part) visible

Range-based approachessonar [Stoeter et al.1995], stereo [Kim et al. 1994], laser [Anguelov et al. 2004]

Vision-based approaches

lintel

post

What is Lintel-Occluded?Lintel-occluded post-and-lintel architecture camera is low to ground cannot point upward b/c obstacles

Our Approach

,)(sign)(N

1nn

xhx n

n

,)(sign)(N

1nn

xhx n

n

Assumptions:• Both door posts are visible• Posts appear nearly vertical• The door is at least a certain width

Key idea: Multiple cues are necessary for robustness (pose, lighting, …)

Video

Pairs of Vertical Lines

1. Edges detected by Canny2. Line segments detected by modified Douglas-Peucker algorithm3. Clean up (merge lines across small gaps, discard short lines)4. Separate vertical and non-vertical lines5. Door candidates given by all the vertical line pairs whose spacing

is within a given range

Canny edges detected lines

vertical lines

non-vertical lines

Homography

In the world In the image

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x

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hhh

hhh

y

x

Hy

x

(x’, y’) (x, y)

Prior Model Features: Width and Height

3.0

y

x

Principal point

(0,0)(0,0)(0,y) (x, y)

(x,0)

? ?

1 CHH T

An Example

As the door turns, the bottom corner traces an ellipse (projective transformation of circle is ellipse)

But not horizontal

Data Model (Posterior) Features

Image gradient along edges (g1)Placement of top and bottom edges (g2 , g3)

Color (g4)

texture (g5) Kick plate (g6)Vanishing point (g7)

and two more…

Data Model Features (cont.)Intensity along the line

darker (light off)

positive

negative

brighter (light on)

no gap

Bottom gap(g8)

Data Model Features (cont.)

wall walldoor

Slim “U”

intersection line of wall and floor

extension of intersection line

bottom door edge

vertical door lines

ε

Lleft

LRight

floor

Concavity(g9)

The strong classifier

Two Methods to Detect Doors

}1,1{,classifier weak eachfor

decison hard the is where

,0)(sgn)(ψ1

n

n

N

nnn

h

h

xhx

Adaboost

Weights of features

Bayesian formulation

Training images

)d()d|()d( priordata IE

Weights of features

(yields better results)

Bayesian Formulation

)d()d|()|d( pIpIp door image

Taking the log likelihood,

)d()d|()d( priordata IE

)d()d|()|d( pIpIp door image

data

1data )d(),d(

N

iii fI

prior

1prior )d()d(

N

jjjg

Data model Prior model

]1,0[)d(g and (d) where j if

MCMC and DDMCMC• Markov Chain Monte Carlo (MCMC) is used here to maximize

probability to detect door (like random walk through state space of doors)

• Data driven MCMC (DDMCMC) is used to speed up computation doors appear more frequently at the position close to the vertical lines the top of the door is often occluded or a horizontal line closest to the top the bottom of the door is often close to the wall/floor boundary.

Experimental Results: Similar or Different Door/Wall Color

Experimental Results: High Reflection / Textured Floors

Experimental Results: Different Viewpoints

Experimental Results: Cluttered Environments

Results

25 different buildings 600 images:• 100 training• 500 testing

91.1% accuracy with 0.09 FP per image

Speed: 5 fps 1.6GHz (unoptimized)

False Negatives and Positives

distracting reflectionconcavity and bottom gap tests fail

strong reflection

concavity erroneously detected

two vertical lines unavailable

distracting reflection

Navigation in a Corridor• Doors were detected and tracked from frame to frame.• Fasle positives are discarded if doors were not

repeatedly detected.

Video

Conclusion• Path following

• Teach-replay, comparing image coordinates of feature points (no calibration)

• Qualitative decision rule (no Jacobians, homographies)

• Person following• Detects and matches feature points between a stereo pair of

images and between successive images.

• RANSAC-based procedure to estimate the motion of each region

• Does not require the person to wear a different color from the background.

• Door detection• Integrate a variety of features of door

• Adaboost training and DDMCMC.

Future Work• Path following

Incorporating higher-level scene knowledge to enable obstacle avoidance and terrain characterization

Connecting multiple teaching paths in a graph-based framework to enable autonomous navigation between arbitrary points.

• Person following Fusing the information with additional appearance-based

information ( template or edges) . Integration with EM tracking algorithm.

• Door detection Calibrate the camera to enable pose and distance measurements

to facilitate the building of a geometric map. Integrated into a complete navigation system that is able to drive

down a corridor and turn into a specified room