Scalix: Uncertainty-Aware Scale-Consistent Monocular SLAM

2026-08-18

AI Summary

本文提出Scalix，一种实时单目SLAM框架，通过将学习到的深度线索整合进概率图模型中解决尺度不确定性问题，提高尺度一致性。

Abstract

Cameras are ubiquitous sensors in robotics due to their compact form factor and the perceptual richness captured through visual information. Monocular SLAM enables robots to understand the environment with a minimum setup, however, it inherently suffers from scale ambiguity. A common solution is to provide multi-modal sensor configurations, such as visual-inertial systems, where scale is observable unless the robot navigates under a constant-velocity motion, a common scenario in mobile robotics. With the advent of deep-learning, geometric foundation models have been used to address this problem, but the depths maps are often noisy and scale-inconsistent across frames. In this paper, we propose Scalix, a real-time monocular SLAM framework that achieves metric-scale state estimation by integrating learned depth cues into a probabilistic factor-graph formulation. By augmenting existing monocular depth models with both per-pixel depth uncertainty and per-frame scale uncertainty, Scalix treats scale predictions as independent measurements within its optimization, leading to improved scale consistency through multi-view data associations. Experiments in large-scale outdoor and indoor environments demonstrate state-of-the-art performance on both metric and up-to-scale benchmarks while maintaining real-time operation and generalization.

Problem

• Monocular SLAM
• Scale Ambiguity
• Depth Uncertainty

Innovation

• monocular SLAM
• scale consistency
• depth uncertainty
• factor-graph formulation
• real-time