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. Montgomery Robotics and Automated Systems 2001 “Vision Guided Landing of an Autonomous Helicopter in Hazardous Terrain” Andrew Johnson, James Montgomery and Larry Matthies International 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. Matthies Journal of Field Robotics 2006 “Lidar-based Hazard Avoidance for Safe Landing on Mars” Andrew Johnson, Allan Klumpp, James Collier and Aron Wolf AIAA 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 Avoidance for 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 Avoidance for 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 Avoidance for 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