# Uncertainty Estimation of Dense Optical-Flow for Robust Visual Navigation

arXiv: https://arxiv.org/abs/2109.14828 · HTML: https://mlsanity.com/paper/2109.14828/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Semantic Validation in Structure from Motion](https://mlsanity.com/paper/2304.02420/) (2023)

## Abstract (arXiv)

This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localization and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing work has not fully utilized the uncertainty of the optical flow -- at most an isotropic Gaussian density model. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimization, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset.