Dense mapping from sparse visual odometry: a lightweight uncertainty-guaranteed depth completion method.

ABSTRACT:

Introduction

Visual odometry (VO) has been widely deployed on mobile robots for spatial perception. State-of-the-art VO offers robust localization, the maps it generates are often too sparse for downstream tasks due to insufffcient depth data. While depth completion methods can estimate dense depth from sparse data, the extreme sparsity and highly uneven distribution of depth signals in VO (∼ 0.15% of the pixels in the depth image available) poses signiffcant challenges.

Methods

To address this issue, we propose a lightweight Image-Guided Uncertainty-Aware Depth Completion Network (IU-DC) for completing sparse depth from VO. This network integrates color and spatial information into a normalized convolutional neural network to tackle the sparsity issue and simultaneously out

SUBMITTER: Yang D 

PROVIDER: S-EPMC12497602 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature