Perception-Aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments

Kayhani, Navid; Schoellig, Angela P.; McCabe, Brenda

doi:10.1061/jccee5.cpeng-5068

Cited by 4 publications

(1 citation statement)

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“…In [29], the sensors mounted on autonomous agents are considered constant parameters while the poses of fiducial markers in an indoor construction environment are instead treated as design variables. Much like the sensor selection literature, a discrete set of candidate marker poses is considered, and an information-theoretic cost function is used.…”

Section: B Set Function Optimization For Perceptionmentioning

confidence: 99%

Multi-camera sensing for robust perception in robotics

Kaveti¹

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The number and arrangement of sensors on an autonomous mobile robot dramatically influence its perception capabilities. Ensuring that sensors are mounted in a manner that enables accurate detection, localization, and mapping is essential for the success of downstream control tasks. However, when designing a new robotic platform, researchers and practitioners alike usually mimic standard configurations or maximize simple heuristics like field-of-view (FOV) coverage to decide where to place exteroceptive sensors. In this work, we conduct an information-theoretic investigation of this overlooked element of mobile robotic perception in the context of simultaneous localization and mapping (SLAM). We show how to formalize the sensor arrangement problem as a form of subset selection under the E-optimality performance criterion. While this formulation is NP-hard in general, we further show that a combination of greedy sensor selection and fast convex relaxation-based post-hoc verification enables the efficient recovery of certifiably optimal sensor designs in practice. Results from synthetic experiments reveal that sensors placed with OASIS outperform benchmarks in terms of mean squared error of visual SLAM estimates.

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Section: B Set Function Optimization For Perceptionmentioning

confidence: 99%

Multi-camera sensing for robust perception in robotics

Kaveti¹

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SLAM2REF: advancing long-term mapping with 3D LiDAR and reference map integration for precise 6-DoF trajectory estimation and map extension

Vega-Torres,

Braun,

Borrmann

2024

Constr Robot

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This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments. Our proposed framework, SLAM2REF, introduces a novel approach for automatic alignment and map extension utilizing reference 3D maps. The methodology is supported by a sophisticated multi-session anchoring technique, which integrates novel descriptors and registration methodologies. Real-world experiments reveal the framework’s remarkable robustness and accuracy, surpassing current state-of-the-art methods. Our open-source framework’s significance lies in its contribution to resilient map data management, enhancing processes across diverse sectors such as construction site monitoring, emergency response, disaster management, and others, where fast-updated digital 3D maps contribute to better decision-making and productivity. Moreover, it offers advancements in localization and mapping research. Link to the repository: https://github.com/MigVega/SLAM2REF, Data: https://doi.org/10.14459/2024mp1743877.

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