DNN-based SLAM Tracking Error Online Estimation
Simultaneous localization and mapping (SLAM) takes in sensor data, e.g., camera frames, and estimates the user's trajectory while creating a map of the surrounding environment. However, existing SLAM evaluation methods are not reference-free, requiring ground-truth trajectories collected from external systems that are infeasible for most scenarios. In this demo, we present Deep SLAM Error Estimator (DeepSEE), a framework that collects features from a standard visual SLAM pipeline as multivariate time series and uses an attention-based neural network to estimate the tracking error at run time. We evaluate DeepSEE in a game engine-based virtual environment, which generates the visual input for DeepSEE and provides the ground-truth trajectory. Demo participants can navigate the virtual environment to create their own trajectories and view the online pose error estimation. This demo showcases how DeepSEE can act as a quality-of-service indicator for downstream applications.
