Optimal View Path Planning for Visual SLAM

Haner, Sebastian; Heyden, Anders

doi:10.1007/978-3-642-21227-7_35

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…This improves the overall reconstruction of the measurement since viewpoints are selected which improve the laser range data quality of the scanned surface. The work of [14] proposes a continuous optimization of method of finding many future next best view positions to reconstruct the captured environment. The system utilizes a real-time Visual simultaneous localization and mapping (SLAM) approach to estimate the sensor position and map the environment.…”

Section: Related Researchmentioning

confidence: 99%

A probabilistic framework for next best view estimation in a cluttered environment

Potthast

Sukhatme

2014

Journal of Visual Communication and Image Representation

146

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In this article, we present an information gain-based variant of the Next Best View problem for occluded environment. Our proposed method utilizes a belief model of the unobserved space to estimate the expected information gain of each possible viewpoint. More precise, this belief model allows a more precise estimation of the visibility of occluded space and with that a more accurate prediction of the potential information gain of new viewing positions. We present experimental evaluation on a robotic platform for active data acquisition, however due to the generality of our approach it also applies to a wide variety of 3D reconstruction problems. With the evaluation done in simulation and on a real robotic platform, exploring and acquiring data from different environments we demonstrate the generality and usefulness of our approach for Next Best View estimation and autonomous data acquisition.

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Section: Related Researchmentioning

confidence: 99%

A probabilistic framework for next best view estimation in a cluttered environment

Potthast

Sukhatme

2014

Journal of Visual Communication and Image Representation

146

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“…The basic idea is to find the optimal collision-free path of the UAS and its sensor pose in every view, by relying on the best next view (NBV) in an iterative way [15,19,20]. Furthermore, advanced scene understanding and SLAM techniques are also applied [18,21].…”

Section: Introductionmentioning

confidence: 99%

Flight Planning for LiDAR-Based UAS Mapping Applications

Alsadik

Remondino

2020

IJGI

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In the last two decades, unmanned aircraft systems (UAS) were successfully used in different environments for diverse applications like territorial mapping, heritage 3D documentation, as built surveys, construction monitoring, solar panel placement and assessment, road inspections, etc. These applications were correlated to the onboard sensors like RGB cameras, multi-spectral cameras, thermal sensors, panoramic cameras, or LiDARs. According to the different onboard sensors, a different mission plan is required to satisfy the characteristics of the sensor and the project aims. For UAS LiDAR-based mapping missions, requirements for the flight planning are different with respect to conventional UAS image-based flight plans because of different reasons related to the LiDAR scanning mechanism, scanning range, output scanning rate, field of view (FOV), rotation speed, etc. Although flight planning for image-based UAS missions is a well-known and solved problem, flight planning for a LiDAR-based UAS mapping is still an open research topic that needs further investigations. The article presents the developments of a LiDAR-based UAS flight planning tool, tested with simulations in real scenarios. The flight planning simulations considered an UAS platform equipped, alternatively, with three low-cost multi-beam LiDARs, namely Quanergy M8, Velodyne VLP-16, and the Ouster OS-1-16. The specific characteristics of the three sensors were used to plan flights and acquired dense point clouds. Comparisons and analyses of the results showed clear relationships between point density, flying speeds, and flying heights.

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“…The vast majority of the state-of-the-art consists of small variations of the main techniques described above. For example, Haner et al [25] do not only compute the next-best-view, but also take into account a whole set of future imaging locations. Mostegel et al [26] focus more on localization stability rather than reconstruction and Sadat et al [27] focus on maximizing feature richness.…”

Section: Related Workmentioning

confidence: 99%

Effective Exploration for MAVs Based on the Expected Information Gain

Palazzolo

Stachniss

2018

Drones

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Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path.

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Optimal View Path Planning for Visual SLAM

Cited by 12 publications

References 11 publications

A probabilistic framework for next best view estimation in a cluttered environment

A probabilistic framework for next best view estimation in a cluttered environment

Flight Planning for LiDAR-Based UAS Mapping Applications

Effective Exploration for MAVs Based on the Expected Information Gain

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