Towards Online Observability-Aware Trajectory Optimization for Landmark-based Estimators

TL;DR

This paper develops efficient methods for predicting SLAM estimator covariance to enable online, observability-aware trajectory optimization in unknown environments, improving autonomous system localization and perception.

Contribution

It introduces an interval-based filtering approximation, a Lie-derivative measurement bundling scheme, and a landmark contribution aggregation method for efficient covariance prediction in SLAM.

Findings

Enables recursive propagation of ego-covariance with reduced complexity.

Provides computational savings for high-rate sensors like cameras.

Allows prediction of SLAM performance with new landmarks without full linearization.

Abstract

As autonomous systems increasingly rely on onboard sensing for localization and perception, the parallel tasks of motion planning and state estimation become more strongly coupled. This coupling is well-captured by augmenting the planning objective with a posterior-covariance penalty -- however, prediction of the estimator covariance is challenging when the observation model depends on unknown landmarks, as is the case in Simultaneous Localization and Mapping (SLAM). This paper addresses these challenges in the case of landmark- and SLAM-based estimators, enabling efficient prediction (and ultimately minimization) of this performance metric. First, we provide an interval-based filtering approximation of the SLAM inference process which allows for recursive propagation of the ego-covariance while avoiding the quadratic complexity of explicitly tracking landmark uncertainty. Secondly, we introduce a Lie-derivative measurement bundling scheme that simplifies the recursive "bundled" update, representing significant computational savings for high-rate sensors such as cameras. Finally, we identify a large class of measurement models (which includes orthographic camera projection) for which the contributions from each landmark can be directly combined, making evaluation of the information gained at each timestep (nearly) independent of the number of landmarks. This also enables the generalization from finite sets of landmarks $\{\ell^{(n)} \}$ to distributions, foregoing the need for fully-specified linearization points at planning time and allowing for new landmarks to be anticipated. Taken together, these contributions allow SLAM performance to be accurately and efficiently predicted, paving the way for online, observability-aware trajectory optimization in unknown space.