activity
20162023
most citedKalman Filter Tuning with Bayesian Optimization

10 citations · 21 across the 14 of their papers we have counts for

collaborators
Showing 2018Show all

7 papers · 1 filter

cs.RO2018

Extrinisic Calibration of a Camera-Arm System Through Rotation Identification

Steve McGuire, Christoffer Heckman, Daniel Szafir +2

Determining extrinsic calibration parameters is a necessity in any robotic system composed of actuators and cameras. Once a system is outside the lab environment, parameters must b…

cs.LG2018

Factorized Machine Self-Confidence for Decision-Making Agents

Brett W Israelsen, Nisar R Ahmed, Eric Frew +2

Algorithmic assurances from advanced autonomous systems assist human users in understanding, trusting, and using such systems appropriately. Designing these systems with the capaci…

stat.ML2018

Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization

Zhaozhong Chen, Christoffer Heckman, Simon Julier +1

Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time an…

stat.ML2018

Data-Free/Data-Sparse Softmax Parameter Estimation with Structured Class Geometries

Nisar Ahmed

This note considers softmax parameter estimation when little/no labeled training data is available, but a priori information about the relative geometry of class label log-odds bou…

cs.AI2018

Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach

Luke Burks, Ian Loefgren, Nisar Ahmed

This work develops novel strategies for optimal planning with semantic observations using continuous state partially observable markov decision processes (CPOMDPs). Two major innov…

cs.RO2018

Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

Luke Burks, Ian Loefgren, Luke Barbier +4

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large…