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Found 5 papers, 0 papers with code
High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization
However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories.
VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics
In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future.
Competence-Aware Path Planning via Introspective Perception
First, perception errors are learned in a model-free and location-agnostic setting via introspective perception prior to deployment in novel environments.
Visual Representation Learning for Preference-Aware Path Planning
We introduce Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an alternative approach that overcomes all three limitations: VRL-PAP leverages unlabeled human demonstrations of navigation to autonomously generate triplets for learning visual representations of terrain that are viewpoint invariant and encode terrain types in a continuous representation space.
IV-SLAM: Introspective Vision for Simultaneous Localization and Mapping
Existing solutions to visual simultaneous localization and mapping (V-SLAM) assume that errors in feature extraction and matching are independent and identically distributed (i. i. d), but this assumption is known to not be true -- features extracted from low-contrast regions of images exhibit wider error distributions than features from sharp corners.
