Scalix: Uncertainty-Aware Scale-Consistent Monocular SLAM

• URL: http://arxiv.org/abs/2608.17553v1
• Date: Tue, 18 Aug 2026 09:14:37 GMT
• Title: Scalix: Uncertainty-Aware Scale-Consistent Monocular SLAM
• Abstract summary: Scalix is a real-time monocular SLAM framework that achieves metric-scale state estimation.<n> Experiments in large-scale outdoor and indoor environments demonstrate state-of-the-art performance.
• Score: 17.864571450291287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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