Sparse feature-based maps provide a compact representation of the environment that admit efficient algorithms, for example simultaneous localization and mapping. These representations typically assume a static world and therefore contain static map features. However, since the world contains dynamic elements, determining when map features no longer correspond to the environment is essential for long-term utility. This work develops a feature-based model of the environment which evolves over time through feature persistence. Moreover, we augment the state-of-the-art sparse mapping model with a correlative structure that captures spatio-temporal properties, e.g. that nearby features frequently have similar persistence. We show that such relationships, typically addressed through an ad hoc formalism focusing only on feature repeatability, are crucial to evaluate through a probabilistically principled approach. The joint posterior over feature persistence can be computed efficiently and used to improve online data association decisions for localization. The proposed algorithms are validated in numerical simulation and using publicly available data sets.

We present a solution for online simultaneous localization and mapping (SLAM) self-calibration in the presence of drift in calibration parameters in order to support accurate long-term operation. Calibration parameters such as the camera focal length or camera-to-IMU extrinsics are frequently subject to drift over long periods of operation, inducing cumulative error in the reconstruction. The key contributions are modeling calibration parameters as a spatiotemporal quantity: sensor-to-sensor spatial calibration and sensor intrinsic parameters are continuously time-varying, with statistical tests for change detection and regression. An analysis of the long term effects of inappropriately modeling time-varying sensor calibration is also provided. Constant-time operation is achieved by selecting only a fixed number of informative segments of the trajectory for calibration parameter estimation, giving the added benefit of avoiding early linearization errors by not rolling past measurements into a prior distribution. Our approach is validated with simulated and real-world data.