Layered Mission and Path Planning for MAV Navigation with Partial Environment Knowledge

Nieuwenhuisen, Matthias; Behnke, Sven

doi:10.1007/978-3-319-08338-4_23

Cited by 22 publications

(16 citation statements)

References 25 publications

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“…The collision-free path P can be a simple straight line if there are no obstacles along it, or can be computed by a fast, low level path planner such as the one by Nieuwenhuisen and Behnke (2016).…”

Section: Information Gain-based Explorationmentioning

confidence: 99%

“…This critical time value tcritical is dynamically computed according to the trajectory needed to move the MAV to the starting point from the current location to enable a controlled landing. On our system, this trajectory is computed by a low-level planner (Nieuwenhuisen and Behnke, 2016). Once the time-dependent cost function is enabled, it favors viewpoints for the MAV to:…”

Section: Time-dependent Cost Functionmentioning

confidence: 99%

See 1 more Smart Citation

Information-Driven Autonomous Exploration for a Vision-Based Mav

Palazzolo

Stachniss

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Most micro aerial vehicles (MAV) are flown manually by a pilot. When it comes to autonomous exploration for MAVs equipped with cameras, we need a good exploration strategy for covering an unknown 3D environment in order to build an accurate map of the scene. In particular, the robot must select appropriate viewpoints to acquire informative measurements. In this paper, we present an approach that computes in real-time a smooth flight path with the exploration of a 3D environment using a vision-based MAV. We assume to know a bounding box of the object or building to explore and our approach iteratively computes the next best viewpoints using a utility function that considers the expected information gain of new measurements, the distance between viewpoints, and the smoothness of the flight trajectories. In addition, the algorithm takes into account the elapsed time of the exploration run to safely land the MAV at its starting point after a user specified time. We implemented our algorithm and our experiments suggest that it allows for a precise reconstruction of the 3D environment while guiding the robot smoothly through the scene.

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Section: Information Gain-based Explorationmentioning

confidence: 99%

Section: Time-dependent Cost Functionmentioning

confidence: 99%

Information-Driven Autonomous Exploration for a Vision-Based Mav

Palazzolo

Stachniss

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…been conducted in (Daftry et al, 2015), where near-real-time reconstruction is performed and an online indication of redundancy supports the pilot during manual flight. (Nieuwenhuisen & Behnke, 2016) describe a volumetric approach to autonomously navigate an UAV along camera stations for building mapping. The used UAV is equipped with a variety of sensors, enabling navigation between specific mission waypoints on two levels -a global routing based on prior knowledge represented as a static map and a local rerouting to avoid dynamic or unknown obstacles observed by the sensors.…”

Section: Flight Mission Planningmentioning

confidence: 99%

“…Similarly, our work bases on a volumetric representation of the surrounding of a building, which in our case is given by a 2.5D DSM and 2D polygons representing building ground plans and no-trespass areas. In contrast to (Nieuwenhuisen & Behnke, 2016), we automatically derive flight paths and camera stations from the input data. A flight assistant app for mobile devices supports the pilot during the execution of the flight mission, reducing the pilot's workload to supervising the flight and, if necessary, applying simple corrections to the overall trajectory.…”

Section: Flight Mission Planningmentioning

confidence: 99%

A Mobile Multi-Sensor Platform for Building Reconstruction Integrating Terrestrial and Autonomous Uav-Based Close Range Data Acquisition

Cefalu

Haala

Schmohl

et al. 2017

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

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ABSTRACT:Photogrammetric data capture of complex 3D objects using UAV imagery has become commonplace. Software tools based on algorithms like Structure-from-Motion and multi-view stereo image matching enable the fully automatic generation of densely meshed 3D point clouds. In contrast, the planning of a suitable image network usually requires considerable effort of a human expert, since this step directly influences the precision and completeness of the resulting point cloud. Planning of suitable camera stations can be rather complex, in particular for objects like buildings, bridges and monuments, which frequently feature strong depth variations to be acquired by high resolution images at a short distance. Within the paper, we present an automatic flight mission planning tool, which generates flight lines while aiming at camera configurations, which maintain a roughly constant object distance, provide sufficient image overlap and avoid unnecessary stations. Planning is based on a coarse Digital Surface Model and an approximate building outline. As a proof of concept, we use the tool within our research project MoVEQuaD, which aims at the reconstruction of building geometry at sub-centimetre accuracy.

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“…It is equipped with a self designed Navigation Board that is combin ing sensors like a GPS receiver 1 , a barometric altime 1 µ blox, LEA 5T. ter, a magnetic field sensor and an Inertial Measure ment Unit (IMU), consisting of three gyroscopes 2 and a three axis accelerometer 3 . In addition, the payload comprises two cameras 4 and one laser rangefinder 5 , as can be seen in Figure 2.…”

Section: Hardwarementioning

confidence: 99%