Authors:
Zachary Mueller
and
Sotirios Diamantas
Affiliation:
Department of Engineering and Computer Science, Tarleton State University, Texas A&M University System, Box T-0390, Stephenville, TX 76402, U.S.A.
Keyword(s):
Place Recognition, Optical Flow, Optical Flow Fingerprints, Loop Closing.
Abstract:
In this research we present a novel method for place recognition that relies on optical flow fingerprints of features. We make no assumptions about the properties of features or the environment such as color, shape, and size, as we approach the problem parsimoniously with a single camera mounted on a robot. In the training phase of our algorithm an accurate camera model is utilized to model and simulate the optical flow vector magnitudes with respect to velocity and distance to features. A lognormal distribution function, that is the result of this observation, is used as an input during the testing phase that is taking place with real sensors and features extracted using Lucas-Kanade optical flow algorithm. With this approach we have managed to bridge the gap between simulation and real-world environments by transferring the output of simulated training data sets to real testing environments. In addition, our method is highly adaptable to different types of sensors and environments.
Our algorithm is evaluated both in indoor and outdoor environments where a robot revisits places from different poses and velocities demonstrating that modeling an unknown environment using optical flow properties is feasible yet efficient.
(More)