3D Perception and Environment Map Generation for Humanoid Robot Navigation A DISCUSSION OF: -BY ANGELA FILLEY

Main Problem Given a humanoid robot that can: o Walk around obstacles o Go up and down stairs o Crawl underneath obstacles Want to create a system for generating 3D environmental maps from stereo vision data Done by segmenting range data into planar segments using modified version of the scan-line grouping algorithm and building map from segments!

Quick Definitions Stereo Vision: Getting 3D information from digital images, using two vantage points (like eyes!) Range Image: An image containing depth information (the distance to points in a scene from a specific point) for each pixel Range Segmentation: Diving those images so that all points of the same surface belong to same segment (or region) - that there is no overlap between different regions and then the union of all segments produces the entire image

Structure of Paper 1.Updating segmentation algorithm 2.Generating 3D map from that segmentation 3.Experimental test on QRIO robot

Classic Scan-line Grouping Algorithm Main idea: Points on a scan line in the range image form a straight line if they belong to the same planar region. ◦Each scan line defines a plane passing through the focal point ◦A straight line segment is observed when the planar region intersects with an environment plane ◦Extract line segments in each scan line ◦Region growing using the line segments as primitives

Original Split Algorithm N min = minimum number of points for defining a line segment d cord = the threshold, distance to the origin so all points on plane satisfy plane plane equation k = index of furthest point

Algorithm improvements Line Extraction: Least squares estimation applied to input points to create line, fit of the line checked Find Seed Region: Look for triple of neighboring line segments that are neighbors overlapping in their indices Region Growing: Look for neighboring lines in seed region Post Processing: ◦Check line segments at border plane to see if fits better to a neighboring plane ◦Check single points at the border of a plane fit better to a neighboring plane ◦Check to see if can merge planes ◦Check if can remove border points to remove noise

New Split Algorithm

Split algorithm comparison The OG split algorithm best when noise in data is constant over the whole image. Splits graph a) well - however too much noise in b) even though linear regression line is same with same standard deviation The rd-split algorithm advantage is that can handle more noise and mainly depends on complexity of scene

New algorithm vs patchlet approach Generally more accurate segmentation: ◦Areas close to sensor: usually more precise ◦Areas with more noise (farther away): because allows for a coarse segmentation, the overall accuracy can drop Biggest advantage: processing time of algorithm is in milliseconds vs several minutes for patchlet approach

Building a map from sensor data that the robot can use Combines coarse 3D occupancy grid with the plane information from segmentation process! Benefits: robustness to sensor noise and provides precise floor heights

Assumptions that go into environment generation 1. The world is separated into floor and obstacles 2. The floor is planar and horizontal (or then it’s an obstacle) 3. There are no multiple floor levels at the same location 4. The robot can distinguish between floor and obstacles 5. The robot can estimate the floor and obstacles' relative positions and heights using its sensors

Grid classification Algorithm Types of environment cells (with predefined height parameters): ◦floor: even surface the robot can step on ◦stairs: small change in floor height ◦border: large change in floor height ◦tunnel: low ceiling above the floor ◦obstacle: an obstacle the robot has to avoid ◦unknown: unclassified terrain.

a)Simulated world system with three floor levels and two obstacles b)Floor height map, obstacle heights unknown and cannot be segmented into planes c)3D grid but with tops of obstacles assumed flat like floor d)Complete Floor/Obstacle Grid

Apply to Sony’s QRIO! Video:

Conclusion / Results Outperformed (then) state-of-the-art approaches/competitors in: ◦number of reported segments ◦segmentation accuracy ◦runtime for computing results Because approach accesses each data point only during line extraction and post-processing steps: ◦The algorithm is very efficient ◦Can be employed in real-time systems with large image sizes