---
abstract: |
    This paper presents a system capable of autonomously mapping the visible part of a bounded three-dimensional structure using a mobile ground robot equipped with a depth sensor. We describe motion planning strategies to determine appropriate successive viewpoints and attempt to fill holes automatically in a point cloud produced by the sensing and perception layer. We develop a local motion planner using potential fields to maintain a desired distance from the structure. The emphasis is on accurately reconstructing a 3D model of a structure of moderate size rather than mapping large open environments, with applications for example in architecture, construction and inspection. The proposed algorithms do not require any initialization in the form of a mesh model or a bounding box. We compare via simulations the performance of our policies to the classic frontier based exploration algorithm. We illustrate the efficacy of our approach for different structure sizes, levels of localization accuracy and range of the depth sensor.

    *Note to Practitioners—* The objective of this work is to automate the process of building a 3D model of a structure of interest that is as complete as possible, using a mobile depth sensor, in the absence of any prior information about this structure. Given that several robust solutions for the Visual Simultaneous Localization and Mapping problem (vSLAM) are readily available, the key challenge that we address here is to develop motion planning policies to control the trajectory of the sensor. We present a system that is capable of autonomously determining the boundaries of the structure, before attempting to fill the holes in the constructed model. The performance of our system is illustrated through simulations performed with a depth sensor carried by a mobile manipulator.
author:
- 'Manikandasriram Srinivasan Ramanagopal,  and Jerome Le Ny, [^1][^2]'
bibliography:
- 'IEEEabrv.bib'
- 'biblio.bib'
title: |
    Motion Planning Strategies for\
    Autonomously Mapping 3D Structures
---

Motion Planning, Active Sensing, Active SLAM, Autonomous Mapping, Autonomous Inspection

We divide our mapping process into two phases. The first phase is the Perimeter Exploration (PE) phase, during which the robot moves clockwise around the structure to determine its boundaries. The robot continuously moves towards previously unseen regions of the structure, with the exploration directed towards finding the limits of the structure rather than closely following its geometry. The PE phase ends when our algorithm detects that the robot has returned to the neighborhood of its starting point ${\mathsf{O}}$ and the vSLAM module detects a global loop closure. After completing the PE phase, the system determines the locations of potential missing parts in the constructed 3D model. We can then start the second phase, which we call the Cavity Exploration (CE) phase, during which the system explores these missing parts in the model. The following subsections explain each step of our process in detail.

Conclusion and Future Work
==========================

This paper presents novel motion planning policies that guide a mobile ground robot carrying a depth sensor to autonomously explore the visible portion of a bounded three-dimensional structure. The proposed policies do not assume any prior information about the size or geometry of the structure. Coupled with state-of-art vSLAM systems, our strategies are able to achieve high coverage in the reconstructed model, given the physical limitations of the platform. We have illustrated the efficacy of our approach via 3D simulations for different structure sizes, camera range and localization accuracy. In addition, a comparison of our policies with the classic frontier based exploration algorithms clearly shows the improvement in performance for a realistic structure such as a house.

This work opens interesting questions such as combined vehicle and 6-DOF arm motion planning to map fine structural details. Extending our algorithms to aerial platforms and hybrid teams of robots, for example, would also allow for autonomous inspection of tall structures such as wind turbines or telecom towers.

[^1]: Part of this work was performed while the first author was visiting Polytechnique Montreal, under a Globalink Fellowship from MITACS. This work was also supported by NSERC (Grant 435905-13) and the Canada Foundation for Innovation (Grant 32848).

[^2]: M. S. R. is with the Department of Electrical Engineering, IIT Madras, Chennai, India. J. Le Ny is with the Department of Electrical Engineering, Polytechnique Montreal, and GERAD, Montreal, QC H3T 1J4, Canada. e-mails: [srmanikandasriram@gmail.com, jerome.le-ny@polymtl.ca]{}.
