Zobrazeno 1 - 10
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pro vyhledávání: '"Brink, Kevin"'
Localization in GPS-denied environments is critical for autonomous systems, and traditional methods like SLAM have limitations in generalizability across diverse environments. Magnetic-based navigation (MagNav) offers a robust solution by leveraging
Externí odkaz:
http://arxiv.org/abs/2407.19046
It is common in pose graph optimization (PGO) algorithms to assume that noise in the translations and rotations of relative pose measurements is uncorrelated. However, existing work shows that in practice these measurements can be highly correlated,
Externí odkaz:
http://arxiv.org/abs/2404.00010
UAV missions often require specific geometric constraints to be satisfied between ground locations and the vehicle location. Such requirements are typical for contexts where line-of-sight must be maintained between the vehicle location and the ground
Externí odkaz:
http://arxiv.org/abs/2402.03135
Autor:
Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, Fang, Ruogu
Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could
Externí odkaz:
http://arxiv.org/abs/2308.10453
Autor:
Ganesh, Prashant, Ramos, J. Humberto, Goecks, Vinicius G., Paquet, Jared, Longmire, Matthew, Waytowich, Nicholas R., Brink, Kevin
This paper describes a methodology for learning flight control systems from human demonstrations and interventions while considering the estimated uncertainty in the learned models. The proposed approach uses human demonstrations to train an initial
Externí odkaz:
http://arxiv.org/abs/2305.00929
Infrared thermography has been widely used in several domains to capture and measure temperature distributions across surfaces and objects. This methodology can be further expanded to 3D applications if the spatial distribution of the temperature dis
Externí odkaz:
http://arxiv.org/abs/2304.03930
Autor:
Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, Fang, Ruogu
Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when th
Externí odkaz:
http://arxiv.org/abs/2302.05142
Pose graph optimization is a special case of the simultaneous localization and mapping problem where the only variables to be estimated are pose variables and the only measurements are inter-pose constraints. The vast majority of pose graph optimizat
Externí odkaz:
http://arxiv.org/abs/2209.07626
Autor:
Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, Fang, Ruogu
Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated,
Externí odkaz:
http://arxiv.org/abs/2209.06077
Autor:
Ramos, J. Humberto, Shin, Jaejeong, Volle, Kyle, Buzaud, Paul, Brink, Kevin, Ganesh, Prashant
In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumulation of positional error which can interfere with reliable performance. Improved navigational accuracy without GPS enables vehicles to achieve a
Externí odkaz:
http://arxiv.org/abs/2208.00988