Object Recognition and Semantic Mapping for Underwater Vehicles Using Sonar Data


The application of robots as a tool to explore underwater environments has increased in the last decade. Underwater tasks such as inspection, maintenance, and monitoring can be automatized by robots. The understanding of the underwater environments and the object recognition are required features that are becoming a critical issue for these systems. On this work, a method to provide a semantic mapping on the underwater environment is provided. This novel system is independent of the water turbidity and uses acoustic images acquired by Forward-Looking Sonar (FLS). The proposed method efficiently segments and classifies the structures in the scene using geometric information of the recognized objects. Therefore, a semantic map of the scene is created, which allows the robot to describe its environment according to high-level semantic features. Finally, the proposal is evaluated in a real dataset acquired by an underwater vehicle in a marina area. Experimental results demonstrate the robustness and accuracy of the method described in this paper.

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We thank to CNPq, CAPES, FAPERGS, Oil Brazilian Agency, PRH-27 FURG-ANP/MCT and IBP – Brazillian Petroleum, Gas and Biofuels Institute to support this research. This paper is a contribution of the INCT-Mar COI funded by CNPq Grant Number 610012/2011-8 and CAPES-DGPU project BS-NAVLOC (CAPES no 321/15, DGPU 7523/14-9, MEC project PHBP14/00083): Brazil-Spain cooperation on navigation and localization for autonomous robots on terrestrial and underwater environments.

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Correspondence to Matheus dos Santos.

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dos Santos, M., Drews, P., Núñez, P. et al. Object Recognition and Semantic Mapping for Underwater Vehicles Using Sonar Data. J Intell Robot Syst 91, 279–289 (2018). https://doi.org/10.1007/s10846-017-0721-4

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  • Robot vision
  • Underwater robot
  • Semantic mapping
  • Object recognition
  • Forward looking sonar