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Found 5 papers, 2 papers with code
Feature Tracks are not Zero-Mean Gaussian
In state estimation algorithms that use feature tracks as input, it is customary to assume that the errors in feature track positions are zero-mean Gaussian.
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal while avoiding obstacles in uncertain environments.
Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers
We propose a method to estimate the uncertainty of the outcome of an image classifier on a given input datum.
Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance
In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments.
Unsupervised Depth Completion from Visual Inertial Odometry
Our method first constructs a piecewise planar scaffolding of the scene, and then uses it to infer dense depth using the image along with the sparse points.
Ranked #4 on
Depth Completion
on VOID
