Freetures: Localization in Signed Distance Function Maps

Millane, Alexander; Oleynikova, Helen; Lanegger, Christian; Delmerico, Jeff; Nieto, Juan J.; Siegwart, Roland; Pollefeys, Marc; Cadena, César

doi:10.48550/arxiv.2010.09378

Cited by 2 publications

(2 citation statements)

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“…This helps improve computational efficiency and reduce storage requirements while accurately reconstructing the observed scene. TSDFs have been demonstrated in multiple works such as Voxgraph [224], Freetures [225], Voxblox++ [226], or the more recent Voxblox-Field [227]. They can create and maintain globally consistent volumetric maps that are lightweight enough to run on computationally constrained platforms and demonstrate that the resulting representation can navigate unknown environments.…”

Section: Mappingmentioning

confidence: 99%

From SLAM to Situational Awareness: Challenges and Survey

Bavle¹,

Sánchez-López²,

Schmidt³

et al. 2023

Sensors

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The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.

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Section: Mappingmentioning

confidence: 99%

From SLAM to Situational Awareness: Challenges and Survey

Bavle¹,

Sánchez-López²,

Schmidt³

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Point-to-SDF [16], [17] and SDF-to-SDF [18] approaches can be distinguished. Millane et al [19] recently proposed a method for extracting and matching local features directly on the SDF.…”

Section: Related Workmentioning

confidence: 99%

Directional TSDF: Modeling Surface Orientation for Coherent Meshes

Splietker

Behnke

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Real-time 3D reconstruction from RGB-D sensor data plays an important role in many robotic applications, such as object modeling and mapping. The popular method of fusing depth information into a truncated signed distance function (TSDF) and applying the marching cubes algorithm for mesh extraction has severe issues with thin structures: not only does it lead to loss of accuracy, but it can generate completely wrong surfaces. To address this, we propose the directional TSDFa novel representation that stores opposite surfaces separate from each other. The marching cubes algorithm is modified accordingly to retrieve a coherent mesh representation. We further increase the accuracy by using surface gradient-based ray casting for fusing new measurements. We show that our method outperforms state-of-the-art TSDF reconstruction algorithms in mesh accuracy.

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Freetures: Localization in Signed Distance Function Maps

Cited by 2 publications

References 0 publications

From SLAM to Situational Awareness: Challenges and Survey

From SLAM to Situational Awareness: Challenges and Survey

Directional TSDF: Modeling Surface Orientation for Coherent Meshes

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