Building Probabilistic Environment Models from Noisy Sensor Data

1. Building Probabilistic Environment Models from Noisy Sensor Data Edwin Olson eolson@mit.edu Joint work with John Leonard and Seth Teller Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

2. Background Place Recognition Map Optimization The Road Forward Motivation • Vehicle Safety • 40,000 traffic deaths in US annually • Agriculture • Automation • “Precision Agriculture” • Surveying • Unmapped mines • Environmental Monitoring • Mapping • Assistive robots Talos Groundhog Asimo Edwin Olson

3. Background Place Recognition Map Optimization The Road Forward Exciting times! • 2004: Grand Challenge 1 • Off-road terrain, GPS following • Zero teams finish(7.4 / 150 miles) • 2005: Grand Challenge 2 • Off-road terrain, GPS following • Five teams finished (132 miles). • 2007: Grand Challenge 3 • Urban. Moving obstacles. Dynamic route planning. Intersections+Precedence. • Six teams finish (60 miles). November 29, 2014 Edwin Olson 3

4. Background Place Recognition Map Optimization The Road Forward Perception is Central to Success • Perception • Maps • Obstacle avoidance • Path planning Edwin Olson

5. Background Place Recognition Map Optimization The Road Forward Perception is Central to Success Edwin Olson

6. Background Place Recognition Map Optimization The Road Forward Shakey the Robot (1972) • Exceptional end-to-end capabilities • Vision based • Path planning • Natural language interface • Controlled, limited environments • Basic perception Edwin Olson

7. Background Place Recognition Map Optimization The Road Forward State of the Art • Limited environments • DARPA Urban Challenge: map provided in advance • Nobody has produced a global environmental map! • RoboCup: color-coded, known environment Edwin Olson

8. Background Place Recognition Map Optimization The Road Forward Holy Grail • Robots that can reliably operate almost anywhere • Complex, dynamic environments • Scalable • Unknown environments • Operate for extended periods without gross failures • Requires very low error rates Capability Robust, capablerobots 2007 Shakey 1970 2005 2004 Sojourner 1997 Environmentdifficulty Edwin Olson

9. Background Place Recognition Map Optimization The Road Forward Mapping as a proving ground for perception • Mapping • Most fundamental application of perception • Highly sensitive to perceptual failures • Essential for key robot capabilities • Obstacle avoidance • Path Planning

10. Background Place Recognition Map Optimization The Road Forward The Big Picture t=0: Robot drives around… Edwin Olson

11. Background Place Recognition Map Optimization The Road Forward The Big Picture t=1: Robot observes a landmark Edwin Olson

12. Background Place Recognition Map Optimization The Road Forward The Big Picture t=2 : Robot continues exploring … accumulates position error… Edwin Olson

13. Background Place Recognition Map Optimization The Road Forward The Big Picture Place Recognition ? t=3 : Robot observes landmark again, but it’s not where it was expected! Edwin Olson

14. Background Place Recognition Map Optimization The Road Forward The Big Picture Place Recognition ? t=3 : Robot observes landmark again, but it’s not where it was expected! Edwin Olson

15. Background Place Recognition Map Optimization The Road Forward The Big Picture Map Optimization t=3 : The landmark is used to constrain the relative positions of pose 1 and 3 Edwin Olson

16. Background Place Recognition Map Optimization The Road Forward Parts of Map Building Sensor Data Place Recognition (a.k.a. Data Association, Loop Closing) Map Optimization Pose Graph Posterior Map Edwin Olson

17. Background Place Recognition Map Optimization The Road Forward Sensorobservation Sensorobservation Hypotheses • Hypothesis: a geometrical relationship between two poses • Derived from sensor observations Example 1 Correct Hypothesis Incorrect Hypothesis Edwin Olson

18. Background Place Recognition Map Optimization The Road Forward Recognizing Places Edwin Olson

19. Background Place Recognition Map Optimization The Road Forward Road Map Sensor Data Place Recognition (a.k.a. Data Association, Loop Closing) Map Optimization Pose Graph Posterior Map Edwin Olson

20. Background Place Recognition Map Optimization The Road Forward Our Contribution [Olson2006, Olson2007] Olson et al 2006Olson et al 2007 • Consider a single constraint at a time • Similar to Stochastic Gradient Descent • Robot’s position is integral of its motion • A positional error between nodes (a,b) should affect the motions between them, and thus the positions of all the nodes between (a,b) • Use a motion-based state space Robbins & Monro 1951 ∂fi∂x Wiri Δx= Gradient step for constraint i Edwin Olson

21. Background Place Recognition Map Optimization The Road Forward Sample Optimization Problem • Synthetic problem: 3500 poses, 5600 constraints Truth Initial Edwin Olson

22. Background Place Recognition Map Optimization The Road Forward Gauss Seidel Converges Slowly Edwin Olson

23. Background Place Recognition Map Optimization The Road Forward Our Method (SGD) Edwin Olson

24. Background Place Recognition Map Optimization The Road Forward Conclusions • Robotics has important and immediate applications • Perception is the key open problem • Contributions • Algorithm for “place recognition” • Fast optimization methods Edwin Olson

25. Background Place Recognition Map Optimization The Road Forward Road Forward • Expand performance envelope • Greater capabilities in difficult environments Capability Robust, capablerobots 2007 Shakey 1970 2005 2004 Sojourner 1997 Environmentdifficulty November 29, 2014 Edwin Olson 25

26. Background Place Recognition Map Optimization The Road Forward DARPA Urban Challenge… with just a camera? Road Forward • How do we improve our perception capabilities? • Better low-level methods • Better fusion techniques • New sensors • How do we make the technology affordable? • Low-cost sensors • CPU-efficient methods • Building real systems • Great way to test solutions and find new problems! • Save lives! November 29, 2014 Edwin Olson 26