PixelLaser: Range from texture ISVC '10 11/30/2010 Las Vegas, NV Max Korbel ’13, Michael Leece ’11, Kenny Lei, Nicole Lesperance ’12, Steve Matsumoto ’12, and Zachary Dodds Motivation Pipeline Application From their earliest days (e.g., Horswill’s Polly) robots have used image segmentation to estimate which way to steer next, i.e., the general traversability of the terrain ahead. This project pushes segmentations one step further – to build range scans similar to laser range finders (LRFs). Our approach seeks to make LRFs’ large body of mapping, localization, and navigation algorithms available to a much wider audience through low-cost platforms. These image-segmentation scans can then serve as the basis for off-the-shelf spatial reasoning algorithms such as localization and mapping. Scans from Segments 1 The transformation from segmentation to distance depends on the height, angle, and internal geometry of the camera. Rather than calibrate, we empirically fit a function mapping from image height to range-to-obstacle. Classification We use nearest-neighbors classification on small image patches to determine traversable from untraversable texture. A comparison of the color and texture filters, shown at left, has guided the selection of image-patch descriptors. Training: a training image and the patches indexed in the Kd-tree. Blue (red) patches are (un)traversable. Plot of range vs. row Image descriptors and their redundancy Mapping Our Python port of CoreSLAM yields maps of a quality the same as the original authors’. Original Image Just RGB Statistics RGB and Texture Filter Coreslam results from the “playpen” Classification: the nearest neighbors of one patch and the overall results of classifying a novel image. Examples of classified patches Segmentation Localization Each image is segmented via a multi-resolution search for the bottommost transition from traversable to untraversable texture. We are investigating genetic-algorithm approaches to find the patch-descriptor weights that best segment our images. Currently the most expensive piece of the procedure is the nearest-neighbor lookup within the large K-d tree of remembered patches. Our implementation of Monte Carlo Localization using image-segmentation-based scans shows their power and promise. Platform Elementary! This project uses a netbook with OpenCV and Python atop an iRobot Create. The robot is robust and flexible enough to be our primary outreach platform, too. Note that a LRF would cost many times more than this entire platform! PixelLaser-based MCL Acknowledgments Segmentation: we run at several resolutions to search for transitions in terrain traversability. Range scan: the resulting range scan, shown here as it would look in a top-down view. We gratefully acknowledge support by The Rose Hills Foundation, Baker Foundation, the NSF projects REU #0753306, CPATH #0939149, and funds from HMC.