Towards autonomous airborne mapping of urban environments

Adler, Benjamin; Xiao, Jing

doi:10.1109/mfi.2012.6343030

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Adler, Xiao, and Zhang () describe the CPU‐based implementation for particle simulation and also provide quantitative results by comparing the scanned surface over time using the algorithm against results obtained using scan‐line passes. This implementation was tested on an Intel Xeon E3‐1245 CPU, clocked at 3.30 GHz.…”

Section: Resultsmentioning

confidence: 99%

Autonomous Exploration of Urban Environments using Unmanned Aerial Vehicles

Adler¹,

Xiao

Zhang³

2014

Journal of Field Robotics

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This paper introduces a custom-built unmanned aerial vehicle, capable of autonomous exploration in urban environments. It consists of a multicopter, an inertial navigation system and two 2D laser range finders. In addition to a description of the hardware architecture and individual components being used, the authors also discuss challenges and problems that arose during its construction as well as optimizations and workarounds applied in the course of its development.Also presented is the software architecture, with a focus on a novel algorithm capable of generating multiple next best views (NBVs), sorted by achievable information gain. Although being designed for application on airborne platforms in urban environments, it works directly on raw point clouds and thus can be used with any sensor generating spatial occupancy information (e.g. LIDAR, RGBD-or time-of-flight-cameras). To satisfy constraints introduced by real-time operation on UAVs, the algorithm is implemented on a highly parallel SIMD architecture and benchmarked using GPUs from multiple hardware generations, using data from real flights. It is also compared against the previous, CPU-based proof of concept.As the underlying hardware imposes limitations with regards to memory access and concurrency, necessary data structures and further performance considerations are explained in detail.Open-source code for this paper is available at http://www.github.com/benadler/octocopter/.

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Section: Resultsmentioning

confidence: 99%

Autonomous Exploration of Urban Environments using Unmanned Aerial Vehicles

Adler¹,

Xiao

Zhang³

2014

Journal of Field Robotics

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“…The development of inertial sensors and microcontrollers in the last five years meanwhile led to miniaturized systems, which match requirements for flight control and navigation of UAV. Examinations to use this data for direct georeferencing of mobile sensor data are in progress since several years (Adler et al 2012, Nüchter et al 2013). …”

Section: Motivationmentioning

confidence: 99%

Enhancements in Uav Flight Control and Sensor Orientation

Bäumker

Przybilla

Zurhorst³

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The acquisition of photogrammetric image data by means of Unmanned Aerial Vehicles (UAV) has developed in recent years to an interesting new measurement method especially for small to medium sizes of objects. In addition the latest developments in the field of navigation systems (GNSS), of inertial sensors and other sensors in combination with powerful and easy to program microcontrollers have made a major contribution to this. In particular, the development of MEMS sensors has triggered the boom of the UAV and has given decisively influence and it is still going on. The integration of sensors on a single board not only enables a cost-effective manufacturing and mass production, but also the use in accordance with small, lightweight UAV. The latest developments on a 50 mm x 50 mm-sized circuit board combine the sensors and the microcontroller for the flight control and flight navigation. Both the board and the microcontroller are easy to program and maintain several interfaces for connecting additional sensors, such as GNSS, ultrasonic sensors and telemetry.This article presents the UAV system of the Bochum University of Applied Sciences, the used sensors and the obtained results for accurate georeferencing.

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Designing a compact hexacopter with gimballed lidar and powerful onboard Linux computer

Liew

Yairi

2015

2015 IEEE International Conference on Information and Automation

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Towards autonomous airborne mapping of urban environments

Cited by 4 publications

References 12 publications

Autonomous Exploration of Urban Environments using Unmanned Aerial Vehicles

Autonomous Exploration of Urban Environments using Unmanned Aerial Vehicles

Enhancements in Uav Flight Control and Sensor Orientation

Designing a compact hexacopter with gimballed lidar and powerful onboard Linux computer

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