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Three-Dimensional Mapping with Augmented Navigation Cost through Deep Learning

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Abstract

This work addresses the problem of mapping terrain features based on inertial and LiDAR measurements to estimate navigation cost, for an autonomous ground robot. The navigation cost quantifies the degree of how easy or difficult it is to navigate through different areas. Unlike most indoor applications, where surfaces are usually human-made, flat, and structured, external environments may be unpredictable as to the types and conditions of the travel surfaces, such as traction characteristics and inclination. Attaining full autonomy in outdoor environments requires a mobile ground robot to perform the fundamental localization and mapping tasks in unfamiliar environments, but with the added challenge of unknown terrain conditions. Autonomous motion in uneven terrain has been widely explored by the research community focusing on one or more of the several factors involved aiming at both safety and efficient displacement. A fuller representation of the environment is fundamental to increase confidence and to reduce navigation costs. To this end we propose a methodology composed of five main steps: (i) speed-invariant inertial transformation; (ii) roughness level classification; (iii) navigation cost estimation; (iv) sensor fusion through Deep Learning; and (v) estimation of navigation costs for untraveled regions. To validate the methodology, we carried out experiments using ground robots in different outdoor environments with different terrain characteristics. Results show that the inertial data transformation reduces the dispersion of signal magnitude for different speeds and scenarios. Meanwhile, the roughness level classification achieved a mean accuracy of 95.4%, for the speed of 0.6 m/s. Finally, the obtained terrain maps are a faithful representation of outdoor environments allowing accurate and reliable path planning.

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Data Availability

Datasets should be made available upon the acceptance and publication.

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Acknowledgments

This work was developed with the support of Conselho Nacional de Desenvolvimento Cientifico e Tecnológicó (CNPq), Coordenacão de Aperfeiçoamento de Pessoal de Nível Superioŗ (CAPES), Fundacão de Amparo à Pesquisa do Estado de Minas Geraiş (FAPEMIG), Fundacão de Amparo à Pesquisa do Estado do Amazonaş (FAPEAM) and Commonwealth Scientific and Industrial Research Organisation (CSIRO).

Funding

- Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

- Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)

- Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM)

- Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Author information

Authors and Affiliations

  1. Computer Vision and Robotics Laboratory (VeRLab), Department of Computer Science, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, 31270-901, Brazil

    Felipe G. Oliveira, Mario F. M. Campos & Douglas G. Macharet

  2. Data 61, Commonwealth Scientific and Industrial Research Organization, Pullenvale, QLD, 4069, Australia

    David Howard & Paulo Borges

  3. Department of Electronic Engineering, UFMG, Belo Horizonte, 31270-901, Brazil

    Armando A. Neto

  4. Institute of Exact Sciences and Technology, Universidade Federal do Amazonas (UFAM), Itacoatiara, 69103-340, Brazil

    Felipe G. Oliveira

Authors
  1. Felipe G. Oliveira
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  2. Armando A. Neto
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  3. David Howard
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  4. Paulo Borges
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  5. Mario F. M. Campos
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  6. Douglas G. Macharet
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Contributions

Felipe G. Oliveira was responsible for the general design and development of the navigation cost estimation and map augmentation. Armando A. Neto contributed in the inertial speed-invariant transformation and data analysis. David Howard and Paulo Borges have contributed with the localization and mapping algorithm, and the data acquisition with the Gator platform. Mario F.M. Campos and Douglas G. Macharet have supervised the work and conducted the writing and revision of the paper.

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Correspondence to Felipe G. Oliveira.

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Oliveira, F.G., Neto, A.A., Howard, D. et al. Three-Dimensional Mapping with Augmented Navigation Cost through Deep Learning. J Intell Robot Syst 101, 50 (2021). https://doi.org/10.1007/s10846-020-01304-y

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