Neural Signed Distance Fields (SDFs) provide a differentiable environment representation to readily obtain collision checks and well-defined gradients for robot navigation tasks. However, updating neural SDFs as the scene evolves entails re-training, which is tedious, time consuming, and inefficient, making it unsuitable for robot navigation with limited field-of-view in dynamic environments. Towards this objective, we propose a compositional framework of neural SDFs to solve robot navigation in indoor environments using only an onboard RGB-D sensor. Our framework embodies a dual mode procedure for trajectory optimization, with different modes using complementary methods of modeling collision costs and collision avoidance gradients. The primary stage queries the robot body's SDF, swept along the route to goal, at the obstacle point cloud, enabling swift local optimization of trajectories. The secondary stage infers the visible scene's SDF by aligning and composing the SDF representations of its constituents, providing better informed costs and gradients for trajectory optimization. The dual mode procedure combines the best of both stages, achieving a success rate of 98%, 14.4% higher than baseline with comparable amortized plan time on iGibson 2.0. We also demonstrate its effectiveness in adapting to real-world indoor scenarios.

翻译：神经符号距离场（SDF）为机器人导航任务提供了可微分环境表征，可直接获取碰撞检测与明确定义的梯度。然而，当场景动态变化时更新神经SDF需要重新训练，这一过程繁琐耗时且效率低下，使其不适用于动态环境中视场受限的机器人导航。针对该问题，我们提出一种神经SDF的组合框架，仅利用机载RGB-D传感器解决室内环境中的机器人导航问题。该框架采用双模式轨迹优化流程，不同模式分别运用互补的碰撞代价建模与避障梯度计算方法。主阶段通过查询机器人本体沿目标路径扫描形成的SDF在障碍物点云中的值，实现轨迹的快速局部优化。次阶段通过对场景构成要素的SDF表征进行对齐与组合，推断可见场景的SDF，为轨迹优化提供信息更完备的代价函数与梯度。双模式流程融合了两阶段的优势，在iGibson 2.0仿真平台上实现了98%的成功率，较基线方法提升14.4%，且具有相当的分摊规划时间。我们还验证了该方法在真实室内场景中的适应有效性。