Predicting Objective Function Change in Pose-Graph Optimization

Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outliers but cannot be computed unless the problem is solved. In this paper, we show how the objective function change can be predicted in an incremental pose-graph optimization scheme, without actually solving the problem. The predicted objective function change can be used to guide online decisions or detect outliers. Experiments validate the accuracy of the predicted objective function, and an application to outlier detection is also provided, showing its advantages over M-estimators.