Monocular vision occupancy grid mapping with obstacle avoidance on UAVs

Emre Balci, Toshinobu Watanabe, Daniel Magree, Thanakorn Khamvilai, Eric Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The dependency of unmanned aerial vehicles (UAVs) on precise knowledge of their environment to fulfill missions without collision has restricted their application until today. Although aerial mapping has already become an astounding economic sector for example in the field of construction checking, surveying, and insurance inspection, there is still more potential for missions needing a higher degree of autonomy. It is comprehensible that developing accurate maps will expand the field of UAV application. Often the mission time and limited payload to bounded electrical energy or fuel is a further restriction. This makes less power consuming and lightweight exteroceptive sensors like the monocular camera especially interesting. This paper introduces a mapping system based on a monocular camera for UAVs. The core of this work is the development of a high accurate, memory efficient, extreme fast and C-language based occupancy map in 3D, operating under real-time constraints using only a single camera. First, a mapping system is constructed, which is based on the structure from motion extended Kalman filter and an occupancy grid filter. Research findings demonstrate that the mapping algorithm is more precise compared to the current state of science. Furthermore, an octree data structure is developed, to store the map efficiently and enable fast algorithms based on this map. The structure stands out of the mass by his compactness and clarity combined with a high efficiency in both memory and processing time.

Original languageEnglish (US)
Title of host publicationAIAA Guidance, Navigation, and Control
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Edition210039
ISBN (Print)9781624105265
DOIs
StatePublished - Jan 1 2018
EventAIAA Guidance, Navigation, and Control Conference, 2018 - Kissimmee, United States
Duration: Jan 8 2018Jan 12 2018

Other

OtherAIAA Guidance, Navigation, and Control Conference, 2018
CountryUnited States
CityKissimmee
Period1/8/181/12/18

Fingerprint

Collision avoidance
Unmanned aerial vehicles (UAV)
Cameras
Data storage equipment
Extended Kalman filters
Insurance
Surveying
Data structures
Inspection
Antennas
Economics
Sensors
Processing

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Balci, E., Watanabe, T., Magree, D., Khamvilai, T., & Johnson, E. (2018). Monocular vision occupancy grid mapping with obstacle avoidance on UAVs. In AIAA Guidance, Navigation, and Control (210039 ed.). American Institute of Aeronautics and Astronautics Inc, AIAA. https://doi.org/10.2514/6.2018-2103
Balci, Emre ; Watanabe, Toshinobu ; Magree, Daniel ; Khamvilai, Thanakorn ; Johnson, Eric. / Monocular vision occupancy grid mapping with obstacle avoidance on UAVs. AIAA Guidance, Navigation, and Control. 210039. ed. American Institute of Aeronautics and Astronautics Inc, AIAA, 2018.
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Balci, E, Watanabe, T, Magree, D, Khamvilai, T & Johnson, E 2018, Monocular vision occupancy grid mapping with obstacle avoidance on UAVs. in AIAA Guidance, Navigation, and Control. 210039 edn, American Institute of Aeronautics and Astronautics Inc, AIAA, AIAA Guidance, Navigation, and Control Conference, 2018, Kissimmee, United States, 1/8/18. https://doi.org/10.2514/6.2018-2103

Monocular vision occupancy grid mapping with obstacle avoidance on UAVs. / Balci, Emre; Watanabe, Toshinobu; Magree, Daniel; Khamvilai, Thanakorn; Johnson, Eric.

AIAA Guidance, Navigation, and Control. 210039. ed. American Institute of Aeronautics and Astronautics Inc, AIAA, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Balci E, Watanabe T, Magree D, Khamvilai T, Johnson E. Monocular vision occupancy grid mapping with obstacle avoidance on UAVs. In AIAA Guidance, Navigation, and Control. 210039 ed. American Institute of Aeronautics and Astronautics Inc, AIAA. 2018 https://doi.org/10.2514/6.2018-2103