1. Qualitative Vision-Based Mobile Robot Navigation Zhichao Chen and Stanley T. Birchfield Dept. of Electrical and Computer Engineering Clemson University Clemson, South Carolina USA

2. Motivation 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]

3. Our approach 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)

4. Preview of results

5. 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 [http://www.ces.clemson.edu/~stb/klt] unknown displacement gray-level images gradient of image

6. Handling lighting changes original modified original Environmental conditions due to clouds blocking sun Automatic gain control of the camera original KLT tracker modified KLT tracker original KLT tracker modified KLT tracker

7. Teach-Replay Teaching Phase start destination detect features track features Replay Phase track features compare features current feature goal feature initial feature goal feature

8. Qualitative decision rule Landmark image plane Feature is to the right |u Current | > |u Goal |  “Turn right” Feature has changed sides sign(u Current ) ≠ sign(u Goal )  “Turn left” No evidence “Go straight” feature funnel lane Robot at goal u Goal u Current

9. Feature is to the right  “Turn right” Side change  “Turn left” The funnel lane at an angle Landmark image plane Robot at goal feature α αα funnel lane No evidence “Go straight”

10. The funnel lane created by multiple feature points α α ambiguous area Landmark #1 Landmark #2 Landmark #3 Feature is to the right  “Turn right” Side change  “Turn left” No evidence “Do not turn”

11. A simplified example “Turn right” “Turn left” “Go straight” Landmark feature Robot at goal funnel lane “Go straight”

12. Qualitative control algorithm Voting scheme Each feature votes either “turn right”, or “turn left” Majority rules Funnel constraints: u Goal u Current u Goal End of segment reached When the mean squared error increases

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

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

15. Experimental results Indoor Outdoor Imaging Source Firewire camera Logitech Pro 4000 webcam

16. Conclusion Approach teach-replay, comparing image coordinates of feature points qualitative decision rule (no Jacobians, homographies) Advantages off-the-shelf camera no calibration (not even lens distortion) simple, easy to implement tested in both indoor and outdoor environments Future work variable driving speed (sharp turns) integration with other sensors (odometry, GPS) obstacle avoidance