Performance evaluation in obstacle avoidance

Nous, Clint; Meertens, Roland; Wagter, Christophe De; Croon, Guido de

doi:10.1109/iros.2016.7759532

Cited by 7 publications

(2 citation statements)

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“…The experimental focus on this study was performed to support the main contribution in the mapping task. The further benchmarking of the trajectory planner in experiments and simulation, for instance, based on the benchmarks (Mettler, Kong, Goerzen, & Whalley, 2010;Nous, Meertens, Wagter, & de Croon, 2016), is left as future work.…”

Section: Future Workmentioning

confidence: 99%

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: Future Workmentioning

confidence: 99%

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Pestana

Maurer

Muschick

et al. 2019

Journal of Field Robotics

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“…The most straight-forward approaches to obstacle detection and depth estimation involve RGB-D or stereo cameras. Unfortunately, these sensors suffer from limited range, in particular stereo systems, that require large baselines to achieve acceptable performances [13]. For example, some authors explored push-broom stereo systems on fixed-wing, high speed MAVs [14].…”

Section: Related Workmentioning

confidence: 99%

J-MOD²: Joint Monocular Obstacle Detection and Depth Estimation

Mancini

Valigi

Ciarfuglia

2018

IEEE Robot. Autom. Lett.

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In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches either rely on Visual SLAM systems or on depth estimation models to build 3D maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multi task architectures to both perform scene understanding and depth estimation. We follow their track and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to exploit the joint information of the obstacle detection task, that produces more reliable bounding boxes, with the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD 2 . We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multi task methods that jointly perform semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments where a MAV explores an unknown scenario and plans safe trajectories by using our detection model.

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