Scalix：感知不确定性的尺度一致单目SLAM
Scalix: Uncertainty-Aware Scale-Consistent Monocular SLAM
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中文总结 AI 辅助
本文提出实时单目SLAM框架Scalix，通过整合带不确定性的学习深度线索到概率因子图，实现度量尺度估计，在多环境基准上性能领先且实时泛化。
中文摘要 AI 辅助
相机因紧凑的外形和视觉信息丰富的感知能力，成为机器人领域中普遍使用的传感器。单目SLAM使机器人以最少的配置理解环境，但天生存在尺度模糊问题。常见解决方案是提供多模态传感器配置，如视觉-惯性系统，不过除非机器人以恒定速度运动（这是移动机器人的常见场景），否则尺度不可观测。随着深度学习的发展，几何基础模型被用于解决该问题，但深度图通常存在噪声且跨帧尺度不一致。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到概率因子图公式中，实现度量尺度的状态估计。Scalix在现有单目深度模型的基础上，同时加入逐像素深度不确定性和逐帧尺度不确定性，将尺度预测作为优化过程中的独立测量值，通过多视图数据关联提升尺度一致性。在大规模室外和室内环境中的实验表明，Scalix在度量基准和尺度适配基准上均达到了最先进的性能，同时保持实时运行能力和泛化性。
英文摘要
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.
发表机构
- University of Technology, Sydney（悉尼科技大学）
- Centre for Autonomous Systems（自主系统中心）
机构由 AI 辅助整理，请以论文原文为准。
