As systems that utilize computer vision move into the public domain, methods of calibration need to become easier to use. Though multi-plane LiDAR systems have proven to be useful for vehicles and large robotic platforms, many smaller platforms and low cost solutions still require 2D LiDAR combined with RGB cameras. Current methods of calibrating these sensors make assumptions about camera and laser placement and/or require complex calibration routines. In this paper we propose a new method of feature correspondence in the two sensors and an optimization method capable of calibration target with unknown lengths in its geometry. Our system is designed with an inexperienced layperson as the intended user, which has lead us to remove as many assumptions about both the target and laser as possible. We show that our system is capable of calibrating the 2-sensor system from a single sample in configurations other methods are unable to handle.

CHS: Small: Socially-Aware Navigation, National Science Foundation PI: David Feil-Seifer, co-PI: Monica Nicolescu, Amount: $500,000, Sept. 1, 2017 - May 31, 2022

REU Site: Collaborative Human-Robot Interaction, National Science Foundation PI: David Feil-Seifer, co-PI: Shamik Sengupta, Amount: $360,000, Feb. 1, 2018 - Jan. 31, 2022