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 this https URL.
Abstract (translated)
我们提出了X-SLAM，一种利用复杂步长有限差分（CSFD）方法实现实时高密度的SLAM系统，无需大型计算图。我们方法的关键是将SLAM过程视为一个可导函数，从而在复数域内通过泰勒级数展开计算重要SLAM参数的导数。我们的系统允许实时计算不仅仅是梯度，还包括高阶导数。这有助于使用高阶优化器实现更好的精度和更快的收敛。在X-SLAM的基础上，我们为两个重要任务实现了端到端优化框架：在广阔户外场景中的相机重新定位和复杂室内环境中主动机器人扫描。在公开基准测试和复杂现实场景的全面评估中，我们的任务感知优化提高了相机重新定位的精度，以及通过我们的优化实现了机器人导航的高效性。代码和数据可在此https URL获取。
URL
https://arxiv.org/abs/2405.02187
