提出可实时评估视觉惯性里程计风险的不确定性框架，无需真值也能提前预测轨迹退化。
SUPER -- A Framework for Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry
- 基于高斯牛顿法中舒尔补块传播不确定性，实现无后端依赖的风险评估。
- 提前50帧预测轨迹退化，准确率比基线提升20%。
- 适用于长时地图构建，支持停机或重定位策略，计算开销低于0.2%。
尽管许多视觉里程计（VO）、视觉惯性里程计（VIO）和SLAM系统具备高精度，但多数现有方法无法在运行时评估风险。本文提出SUPER（基于敏感性的不确定性感知性能与风险评估框架），这是一个通用且可解释的实时风险评估框架，通过敏感性传播不确定性。其科学创新在于推导出一种不依赖后端的实时风险指标，利用高斯牛顿法正规矩阵的舒尔补块传播不确定性。舒尔补捕捉了不确定性对风险发生的影响程度。该框架基于残差大小、几何条件性和短时程时间趋势评估风险，无需地面真值。实验表明，该框架可提前50帧可靠预测轨迹退化，较基线提升20%；同时触发停机或重定位策略的召回率达89.1%。框架与后端无关，运行时额外CPU开销小于0.2%。实验验证了其一致性不确定性估计，并在长时程映射任务中展现适用性。
原文摘要 · Abstract (English)
While many visual odometry (VO), visual-inertial odometry (VIO), and SLAM systems achieve high accuracy, the majority of existing methods miss to assess risks at runtime. This paper presents SUPER (Sensitivity-based Uncertainty-aware PErformance and Risk assessment) that is a generic and explainable framework that propagates uncertainties via sensitivities for real-time risk assessment in VIO. The scientific novelty lies in the derivation of a real-time risk indicator that is backend-agnostic and exploits the Schur complement blocks of the Gauss-Newton normal matrix to propagate uncertainties. Practically, the Schur complement captures the sensitivity that reflects the influence of the uncertainty on the risk occurrence. Our framework estimates risks on the basis of the residual magnitudes, geometric conditioning, and short horizon temporal trends without requiring ground truth knowledge. Our framework enables to reliably predict trajectory degradation 50 frames ahead with an improvement of 20% to the baseline. In addition, SUPER initiates a stop or relocalization policy with 89.1% recall. The framework is backend agnostic and operates in real time with less than 0.2% additional CPU cost. Experiments show that SUPER provides consistent uncertainty estimates. A SLAM evaluation highlights the applicability to long horizon mapping.
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