PROBE-GK: Predictive robust estimation using generalized kernels

By Valentin Peretroukhin, William Vega-Brown, Nicholas A. Roy, Jonathan Kelly. 2016

19 citations recorded by the source corpus.

Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates. In practice, dynamic environments may degrade sensor performance in predictable ways that cannot be captured with static uncertainty parameters. In this paper, we employ fast nonparametric Bayesian inference techniques to more accurately model sensor uncertainty. By setting a prior on observation uncertainty, we derive a predictive robust estimator, and show how our model can be learned from sample images, both with and without knowledge of the motion used to generate the data. We validate our approach through Monte Carlo simulations, and report significant improvements in localization accuracy relative to a fixed noise model in several settings, including on synthetic data, the KITTI dataset, and our own experimental platform.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Kazuya Takeda
2. Alexander Carballo
3. Ekim Yurtsever
4. Jacob Lambert

In Artificial Intelligence

1. Kazuya Takeda
2. Jacob Lambert
3. Alexander Carballo
4. Ekim Yurtsever
5. Junichi Yamagishi
6. Tomoki Toda

In Computer Vision and Pattern Recognition

1. Ekim Yurtsever
2. Alexander Carballo
3. Jacob Lambert
4. Kazuya Takeda

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Both attempting to extract lighting invariant features [147] and assessing the quality of features [148] have been proposed.”