A Contrastive Fewshot RGBD Traversability Segmentation Framework for Indoor Robotic Navigation

TL;DR

This paper introduces a novel multi-modal few-shot segmentation framework combining RGB images and sparse depth data, enhanced with negative contrastive learning, to improve indoor traversability detection for robotic navigation.

Contribution

It proposes a negative contrastive learning branch and a two-stage attention depth module to enhance few-shot RGB-D segmentation accuracy in indoor navigation tasks.

Findings

Outperforms state-of-the-art FSS and RGB-D segmentation methods.

Achieves up to 9% higher mIoU in 1-shot and 5-shot settings.

Effectively leverages negative prototypes and sparse depth for robust segmentation.

Abstract

Indoor traversability segmentation aims to identify safe, navigable free space for autonomous agents, which is critical for robotic navigation. Pure vision-based models often fail to detect thin obstacles, such as chair legs, which can pose serious safety risks. We propose a multi-modal segmentation framework that leverages RGB images and sparse 1D laser depth information to capture geometric interactions and improve the detection of challenging obstacles. To reduce the reliance on large labeled datasets, we adopt the few-shot segmentation (FSS) paradigm, enabling the model to generalize from limited annotated examples. Traditional FSS methods focus solely on positive prototypes, often leading to overfitting to the support set and poor generalization. To address this, we introduce a negative contrastive learning (NCL) branch that leverages negative prototypes (obstacles) to refine free-space predictions. Additionally, we design a two-stage attention depth module to align 1D depth vectors with RGB images both horizontally and vertically. Extensive experiments on our custom-collected indoor RGB-D traversability dataset demonstrate that our method outperforms state-of-the-art FSS and RGB-D segmentation baselines, achieving up to 9\% higher mIoU under both 1-shot and 5-shot settings. These results highlight the effectiveness of leveraging negative prototypes and sparse depth for robust and efficient traversability segmentation.