Sensor data fusion of a redundant dual-platform robot for elevation mapping

Avi Turgeman, Shraga Shoval, Amir Degani

Research output: Contribution to journalArticlepeer-review


This paper presents a novel methodology for localization and terrain mapping along a defined course such as narrow tunnels and pipes, using a redundant unmanned ground vehicle kinematic design. The vehicle is designed to work in unknown environments without the use of external sensors. The design consists of two platforms, connected by a passive, semi-rigid three-bar mechanism. Each platform includes separate sets of local sensors and a controller. In addition, a central controller logs the data and synchronizes the plat-forms’ motion. According to the dynamic patterns of the redundant information, a fusion algorithm, based on a centralized Kalman filter, receives data from the different sets of inputs (mapping techniques), and produces an elevation map along the traversed route in the x-z sagittal plane. The method is tested in various scenarios using simulated and real-world setups. The experimental results show high degree of accuracy on different terrains. The proposed system is suitable for mapping terrains in confined spaces such as underground tunnels and wrecks where standard mapping devices such as GPS, laser scanners and cameras are not applicable.

Original languageEnglish
Pages (from-to)106-116
Number of pages11
JournalJournal of Robotics and Mechatronics
Issue number1
StatePublished - Feb 2018


  • Data fusion
  • Robot control
  • Robotic sensing
  • Robotic terrain mapping
  • Signal estimation


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