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.

翻译：我们提出了X-SLAM，一种实时稠密可微SLAM系统，它利用复步长有限差分（CSFD）方法高效计算数值导数，避免了大规模计算图的依赖。该方法的核心在于将SLAM过程视为可微函数，通过在复数域中展开泰勒级数来计算关键SLAM参数的导数。该系统不仅能实时计算梯度，还能实现高阶微分计算，从而支持使用高阶优化器获得更优精度和更快收敛。基于X-SLAM，我们针对两个重要任务实现了端到端优化框架：室外大场景的相机重定位和复杂室内环境下的主动机器人扫描。在公开基准测试和复杂真实场景中的全面评估表明，通过任务感知优化，相机重定位精度和机器人导航效率均得到显著提升。相关代码和数据已开源至https://gapszju.github.io/X-SLAM。