Lidar Data Reduction for Unmanned Systems Navigation in Urban Canyon

Alfred Mayalu,Barry Jenkins,Kevin Kochersberger,François Malassenet

doi:10.3390/rs12111724

Abstract

This paper introduces a novel protocol for managing low altitude 3D aeronautical chart data to address the unique navigational challenges and collision risks associated with populated urban environments. Based on the Open Geospatial Consortium (OGC) 3D Tiles standard for geospatial data delivery, the proposed extension, called 3D Tiles Nav., uses a navigation-centric packet structure which automatically decomposes the navigable regions of space into hyperlocal navigation cells and encodes environmental surfaces that are potentially visible from each cell. The developed method is sensor agnostic and provides the ability to quickly and conservatively encode visibility directly from a region by enabling an expanded approach to viewshed analysis. In this approach, the navigation cells themselves are used to represent the intrinsic positional uncertainty often needed for navigation. Furthermore, we present in detail this new data format and its unique features as well as a candidate framework illustrating how an Unmanned Traffic Management (UTM) system could support trajectory-based operations and performance-based navigation in the urban canyon. Our results, experiments, and simulations conclude that this data reorganization enables 3D map streaming using less bandwidth and efficient 3D map-matching systems with limited on-board compute, storage, and sensor resources.

Highlights

With increasing use in urban civilian airspace, Unmanned Aircraft Systems (UAS) need to be able to autonomously navigate reliably and safely
By integrating Robot Operating System (ROS) to simulate point cloud acquisition, real-time simulated drone flights enabled the evaluation of the planar feature extraction algorithms and their sensitivity to sensor noise and capture strategies
Instead of relying on fully registered point clouds, the proposed navigation protocol is based on 3D meshes that model static geometry with very high accuracy while only requiring a reduced amount of storage by leveraging planar features

Summary

Introduction

With increasing use in urban civilian airspace, Unmanned Aircraft Systems (UAS) need to be able to autonomously navigate reliably and safely. Companies such as Waymo, Amazon UPS and DHL and, use a variety of sensing modalities for delivery application [1]. As gloabl position satellite (GPS) navigation is subject to severe degradation and blackout in urban areas, an economic and scalable solution to the development of UAS in urban airspace is an efficient and reliable performance-based localization and path planning beyond GPS. During low altitude urban operations, a UAS can frequently encounter portions of a flight path in which GPS precision is significantly limited or GPS service is completely unavailable. A priori knowledge of the 3D environment can be used to predict and avoid challenging conditions at path planning level; the large size of the datasets becomes untenable for small UAS

Methods

Results

Discussion

Conclusion