X-SLAM: Scalable Dense SLAM for Task-aware Optimization using CSFD

TL;DR

X-SLAM introduces a real-time dense SLAM system using complex-step finite difference for efficient derivatives, enabling high-order optimization for improved accuracy in camera relocalization and robotic navigation.

Contribution

The paper presents a novel SLAM system that employs CSFD for efficient differentiation, allowing real-time high-order optimization without large computational graphs.

Findings

Enhanced accuracy in camera relocalization

Improved efficiency in robotic navigation

Effective real-time dense SLAM performance

Abstract

We present X-SLAM, a real-time dense differentiable SLAM system that leverages the complex-step finite difference (CSFD) method for efficient calculation of numerical derivatives, bypassing the need for a large-scale computational graph. The key to our approach is treating the SLAM process as a differentiable function, enabling the calculation of the derivatives of important SLAM parameters through Taylor series expansion within the complex domain. Our system allows for the real-time calculation of not just the gradient, but also higher-order differentiation. This facilitates the use of high-order optimizers to achieve better accuracy and faster convergence. Building on X-SLAM, we implemented end-to-end optimization frameworks for two important tasks: camera relocalization in wide outdoor scenes and active robotic scanning in complex indoor environments. Comprehensive evaluations on public benchmarks and intricate real scenes underscore the improvements in the accuracy of camera relocalization and the efficiency of robotic navigation achieved through our task-aware optimization. The code and data are available at https://gapszju.github.io/X-SLAM.