Evaluation of 3D LiDAR Sensor Setup for Heterogeneous Robot Team


Haddeler G., Aybakan A., Akay M. C. , Temeltaş H.

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, vol.100, no.2, pp.689-709, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 100 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.1007/s10846-020-01207-y
  • Title of Journal : JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • Page Numbers: pp.689-709

Abstract

We encounter a world where the role of robots has increased in society. Day by day, we can observe that problems related to autonomous vehicles are being solved. The focused problem that we have recently confronted is based on collaborative mapping with multi-robots since collaboratively generated map provides more information about the environment than with a single robot. Moreover, the mapping framework for robot team can be more efficient if 3D laser sensors are placed to cover wider area. However, laser sensors are commonly positioned intuitively and depended mostly on the designer's choice. To overcome designer-independent laser sensor setup for efficient collaborative mapping, a subjective evaluation of laser sensor placement is developed as another key aspect. This paper proposed a collaborative mapping framework for heterogeneous robot teams to make full use of collected information from different viewpoints and explore large areas more efficiently. It's comprised of two modules, localization of multi-robots and collaborative map construction. In addition, to avoid arbitrary setup of laser sensors in existing methods whose performance is heavily affected by designers' experience, a metric evaluation system was defined to determine the optimal placements of laser sensors and achieve maximum sweep area. The proposed methods were verified on both simulation and experiments which were carried out on a heterogeneous robot team (including an unmanned aerial vehicle and a ground vehicle). According to the metric evaluation, best placements were chosen for the laser sensors, and presented collaborative mapping technique was shown to work more efficiently compared with existing methods.