A Computationally Efficient Solution to the Simultaneous Localization and Mapping (SLAM) problem in Houses of Mirrors

Dr. Dexter “The Kalman Man” Robotnik1

1 Department of Carnival Robotics, Cranberry-Lemon University, Pittsburgh, PA, USA


Every day people are dying because there is no method for a robot to navigate in a house of mirrors. The Simultaneous Localization and Mapping (SLAM) algorithm has been theoretically developed and optimized for computational efficiency in most environments where robots may need to navigate [1]. It provides an incredibly useful way for robots to navigate inside or outside without the aid of GPS navigation while simultaneously creating elaborate point clouds of their environments in two dimensions and for the gifted, three. However, there has yet to be a widely developed solution to navigating in a GPS denied house of mirrors. The traditional SLAM problem is already computationally taxing enough, there is no feasible way to implement it when all of the feature space is not finite or fixed. In this paper, a novel technique will be developed that will allow a robot to implement a SLAM algorithm in a house of mirrors at a local carnival in a simulated search and rescue operation. This technique is then demonstrated on the Mirror-Realm SLAM Robot (Mrs. Robot).

Keywords: Navigation, Simultaneous Localization and Mapping (SLAM), Robotics, PNT, Fun House Mirrors, Mrs. Robot 

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Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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