Literature Review: Safe Landing Zone Identification

1. Literature Review:Safe Landing Zone Identification Presented by Keith Sevcik

2. Problem Under Investigation • UAV flying in unknown terrain • Typically helicopter • Map terrain • Vision • LIDAR • Identify landing sites • Hazard free • Terrain is suitable • Large enough to fit UAV

3. Papers Reviewed • “Towards Vision-Based Safe Landing for an Autonomous Helicopter”Pedro J. Garcia-Pardo, Gaurav S. Sukhatme and James F. MontgomeryRobotics and Automated Systems 2001 • “Vision Guided Landing of an Autonomous Helicopter in Hazardous Terrain”Andrew Johnson, James Montgomery and Larry MatthiesInternational Conference on Robotics and Automation 2005 • “The JPL Autonomous Helicopter Testbed: A Platform for Planetary Exploration Technology Research and Development”James F. Montgomery, Andrew E. Johnson, Stergios I. Roumeliotis, and Larry H. MatthiesJournal of Field Robotics 2006 • “Lidar-based Hazard Avoidance for Safe Landing on Mars”Andrew Johnson, Allan Klumpp, James Collier and Aron WolfAIAA Journal of Guidance, Control and Dynamics 2002

4. Towards Vision-Based Safe Landing for an Autonomous Helicopter • Platform: Helicopter with Vision System • IMU, Novatel GPS, Engine RPM sensor, color video camera • General Approach: • Locate Obstacles (cars, people, rocks, etc.) • Find location in visible field where the footprint of the helicopter fits between obstacles • Assumptions: • The camera is mounted perpendicular to the plane of the ground, pointing straight down. • The vertical axis of the camera image plane is aligned with the principal axis of the helicopter. • The image shows a higher contrast at obstacle boundaries compared to the boundaries of visual features due to the terrain texture. • The underlying terrain is flat.

5. Towards Vision-Based Safe Landing for an Autonomous Helicopter • Locate Obstacles • Perform thresholding on edge-image • Optimum threshold removes spurious features but leaves obstacle edges • Works if high contrast between obstacles and terrain

6. Towards Vision-Based Safe Landing for an Autonomous Helicopter • Place footprint of helicopter between obstacles • Search the image for a circular area containing pixels below threshold • “Single-frame” analysis • “Multi-frame tracking” analysis • “Multi-frame velocity-vector-based” analysis • “Whole multi-frame” analysis

7. Vision Guided Landing of an Autonomous Helicopter in Hazardous Terrain • Platform: Helicopter with Vision System • Novatel GPS, IMU, compass, roll/pitch inclinometers, laser altimeter, CCD camera • General Approach: • Generate 3D terrain map from consecutive images • Determine surface roughness and slope • Choose area that fits footprint of helicopter and minimizes roughness and slope

8. Vision Guided Landing of an Autonomous Helicopter in Hazardous Terrain • 3D Point Cloud from Consecutive Images • Image is divided into grid and strongest feature is chosen from each grid cell • Correlation used to track features between frames • Direction of motion determined and features fit to previous map • Finer grid of pixels selected • Motion information and sum-of-absolute differences used to locate pixels between frames/get denser terrain info

9. Vision Guided Landing of an Autonomous Helicopter in Hazardous Terrain • Digital Elevation Map • Move from camera frame to surface fixed frame • Relative position between surface and attitude of helicopter calculated • Bounding area determined and divided into bins • Points that lie in bins determined and elevation determined through bilinear interpolation

10. Vision Guided Landing of an Autonomous Helicopter in Hazardous Terrain • Safe Landing Zone • Elevation map partitioned into squares size of lander footprint • Plane fit using least mean squares • Smoothed using interpolation from centers of planes • Roughness is difference between smooth map and DEM • Slope determined from center of each region • Roughness and slope images thesholded, then OR’ed together • Landing zone found in resulting image

11. The JPL Autonomous Helicopter Testbed • Journal paper describing previous paper in greater detail • Employ gantry testbed for tuning algorithms • Extend work to safe site tracking • Also investigate fusion of inertial and vision data for motion estimation

12. Lidar-based Hazard Avoidancefor Safe Landing on Mars • Platform: Hypothetical spacecraft with LIDAR system flying over Mars • Measurements generated with LIDAR model • Terrain generated with Mars model • General Approach: • Resample data into evenly spaced elevation grid • Fit planes to underlying surface ignoring rocks • Calculate slope and roughness • Construct cost map and choose lowest cost area that fits lander footprint as safe landing site

13. Lidar-based Hazard Avoidancefor Safe Landing on Mars • Resampling Data • Makes future calculations easier • Divide data into grid • Convert scanner data into cartesian coordinates • Bilinear interpolation to determine elevation • Surface roughness/slope • LMS to fit planes to underlying surface • Ignore outliers e.g. rocks that skew fit • Roughness based on difference between planes and elevation map

14. Lidar-based Hazard Avoidancefor Safe Landing on Mars • Safe landing zone detection • Create cost map that is product of incidence angle and surface roughness • Limited by max acceptable roughness and angle • Smooth cost map by averaging costs in a region • Safe site is minimization of smoothed cost map