Fast and effective online pose estimation and mapping for UAVs

Schneider, Johannes; Eling, Christian; Klingbeil, Lasse; Kuhlmann, Heiner; Förstner, Wolfgang; Stachniss, Cyrill

doi:10.1109/icra.2016.7487682

Cited by 43 publications

(30 citation statements)

References 28 publications

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“…The system developed by J. Schneider et al () creates a relatively dense georeferenced point‐cloud of very high accuracy while localizing the drone in real time at 100 Hz using only onboard computation on a 3.6 GHz Intel CPU (Santa Clara, CA) with 4 cores. Another possibility to create dense reconstructions is using VO semidense methods, which extract the depth of high‐gradient regions of the scene, such as large‐scale direct LSD‐SLAM (Engel, Schöps, & Cremers, ), Direct Sparse Odometry (DSO; Engel, Koltun, & Cremers, ), or semidense mapping (SDM; Mur‐Artal & Tardós, ).…”

Section: State Of the Artmentioning

confidence: 99%

“…Accuracy evaluation of our obstacle map is performed to provide a basis for the comparison of our obstacle map to that of other methods. Here, it is noted that in some works (e.g., J. Schneider et al, ), the accuracy is evaluated based on the distance of the mapped points to the ground‐truth point‐cloud rather than the other way around, which is not as informative for the purpose of using the 3D reconstruction as an obstacle map.…”

Section: State Of the Artmentioning

confidence: 99%

“…The system developed by J. Schneider et al (2016) creates a relatively dense georeferenced point-cloud of very high accuracy while localizing the drone in real time at 100 Hz using only onboard computation on a 3.6 GHz Intel CPU (Santa Clara, CA) with 4 cores.…”

Section: State Of the Artmentioning

confidence: 99%

See 2 more Smart Citations

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Pestana

Maurer

Muschick

et al. 2019

Journal of Field Robotics

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Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance, in search and rescue scenarios, and on increasing the autonomy of drones in such situations. Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high‐quality sparse map of the environment by using a state‐of‐the‐art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real time on the onboard computer of the drone, a map of size 100m×75m is created in ≈2.75min, therefore, with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approach.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Pestana

Maurer

Muschick

et al. 2019

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…Throughout this work, we assume the pose of the UAV to be known. In practice, we use the pose estimate from a VO/IMU/DGPS combination as described in Schneider et al (2016). Let S be the state of the world.…”

Section: Information Gain-based Explorationmentioning

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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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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“…The aim of this project is to enable autonomous mapping of objects, such as buildings, with an UAV, which plans its trajectory autonomously, detects and avoids obstacles and provides 3D mapping data already during the flight. The UAV, which has been developed within this project, is equipped with digital cameras (Schneider et al, 2016), a laser scanner and a direct georeferencing system Eling et al (2014). The goal of the automation is to optimize the flight path in relation to the intended data acquisition, to reduce the user effort in the processing chain, to enable a real-time mapping with UAVs and to improve the accuracy of the mapping results, especially in urban environments.…”

Section: Introductionmentioning

confidence: 99%

Precise Positioning of Uavs – Dealing With Challenging RTK-GPS Measurement Conditions During Automated Uav Flights

Zimmermann

Eling

Klingbeil

et al. 2017

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

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ABSTRACT:For some years now, UAVs (unmanned aerial vehicles) are commonly used for different mobile mapping applications, such as in the fields of surveying, mining or archeology. To improve the efficiency of these applications an automation of the flight as well as the processing of the collected data is currently aimed at. One precondition for an automated mapping with UAVs is that the georeferencing is performed directly with cm-accuracies or better. Usually, a cm-accurate direct positioning of UAVs is based on an onboard multi-sensor system, which consists of an RTK-capable (real-time kinematic) GPS (global positioning system) receiver and additional sensors (e.g. inertial sensors). In this case, the absolute positioning accuracy essentially depends on the local GPS measurement conditions. Especially during mobile mapping applications in urban areas, these conditions can be very challenging, due to a satellite shadowing, non-line-of sight receptions, signal diffraction or multipath effects. In this paper, two straightforward and easy to implement strategies will be described and analyzed, which improve the direct positioning accuracies for UAV-based mapping and surveying applications under challenging GPS measurement conditions. Based on a 3D model of the surrounding buildings and vegetation in the area of interest, a GPS geometry map is determined, which can be integrated in the flight planning process, to avoid GPS challenging environments as far as possible. If these challenging environments cannot be avoided, the GPS positioning solution is improved by using obstruction adaptive elevation masks, to mitigate systematic GPS errors in the RTK-GPS positioning. Simulations and results of field tests demonstrate the profit of both strategies.

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Fast and effective online pose estimation and mapping for UAVs

Cited by 43 publications

References 28 publications

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Information-Driven Autonomous Exploration for a Vision-Based Mav

Precise Positioning of Uavs – Dealing With Challenging RTK-GPS Measurement Conditions During Automated Uav Flights

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