Bayesian Learning for Safe High-Speed Navigation in Unknown Environments

By Charles Richter, William Vega-Brown, Nicholas A. Roy. 2015

77 citations recorded by the source corpus.

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

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Uncertainty-Aware Reinforcement Learning for Collision Avoidance (2017, methodology): “Uncertainty-aware model-based reinforcement learning has been explored in previous work using Bayesian models [25, 1].”
Safe Visual Navigation via Deep Learning and Novelty Detection (2017, methodology): “In our prior work, we considered model uncertainty in collision prediction, but focused on features of geometric maps, rather than images and neural networks [29].”
Probabilistic Object Detection: Definition and Evaluation (2018, background): “Object detectors will have to quantifying uncertainty for both the reported labels and bounding boxes, which would enable them to be treated as yet another sensor within the established and trusted framework of Bayesian information fusion [39, 47].”
Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing (2018, background): “These submodules can range from mapping and localization [3], [4], to perception [5]–[7], planning [8], [9], and control [10], [11].”
Combining Optimal Control and Learning for Visual Navigation in Novel Environments (2019, background): “lly feasible low-level control for goal-driven behavior. Combining Optimal Control and Learning. A number of papers seek to combine the best of learning with optimal control for high-speed navigation [27, 28, 29, 30, 31]. Drews et al. [28, 32] learn a cost function from monocular images for aggressi”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “How does scene prediction behave in unstructured environments? How to account for uncertainty? Is measurement prediction more reliable and informative than map prediction? How to predict the effect of only a certain set of nonmyopic actions in the map rather than augmenting the whole scene? Regardin”