Seq-DeepIPC：足式机器人导航中用于端到端控制的顺序感知
Seq-DeepIPC: Sequential Sensing for End-to-End Control in Legged Robot Navigation
- Department of Computer Science and Electronics, Universitas Gadjah Mada（计算机科学与电子系，加查马达大学）
- Department of Computer Science and Engineering, Toyohashi University of Technology（计算机科学与工程系，东福冈技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出Seq-DeepIPC模型，通过融合多模态感知（RGB-D+GNSS）与时间序列，实现足式机器人在真实环境中的端到端导航控制，并在机器人狗上验证了其有效性。
AI中文摘要：
我们提出了Seq-DeepIPC，一种用于足式机器人在真实环境中导航的顺序端到端感知到控制模型。Seq-DeepIPC通过将多模态感知（RGB-D+GNSS）与时间融合和控制紧密结合，推进了自主足式导航的智能感知。该模型联合预测语义分割和深度估计，为规划和控制提供更丰富的空间特征。为了在边缘设备上高效部署，我们使用轻量级模型作为编码器，在保持精度的同时减少计算量。通过移除噪声较大的IMU，转而通过顺序GNSS坐标的差分分析推导全局航向，简化了航向估计。我们收集了一个更大且更多样化的数据集，包括道路和草地地形，并在机器人狗上验证了Seq-DeepIPC。对比和消融研究表明，顺序输入改善了我们的模型中的感知和控制，而其他基线则没有受益。Seq-DeepIPC以合理的模型大小取得了具有竞争力或更好的结果；尽管仅使用GNSS的航向在高大建筑物附近可靠性较低，但在开阔区域是鲁棒的。总体而言，Seq-DeepIPC将端到端导航从轮式机器人扩展到更通用和具有时间感知能力的系统。为了支持未来的研究，我们将在GitHub仓库https://github.com/oskarnatan/Seq-DeepIPC发布代码。
英文摘要：
We present Seq-DeepIPC, a sequential end-to-end perception-to-control model for legged robot navigation in real-world environments. Seq-DeepIPC advances intelligent sensing for autonomous legged navigation by tightly integrating multi-modal perception (RGB-D + GNSS) with temporal fusion and control. The model jointly predicts semantic segmentation and depth estimation, giving richer spatial features for planning and control. For efficient deployment on edge devices, we use a lightweight model as the encoder, reducing computation while maintaining accuracy. Heading estimation is simplified by removing the noisy IMU and instead deriving global heading via differential analysis of sequential GNSS coordinates. We collected a larger and more diverse dataset that includes both road and grass terrains, and validated Seq-DeepIPC on a robot dog. Comparative and ablation studies show that sequential inputs improve perception and control in our models, while other baselines do not benefit. Seq-DeepIPC achieves competitive or better results with reasonable model size; although GNSS-only heading is less reliable near tall buildings, it is robust in open areas. Overall, Seq-DeepIPC extends end-to-end navigation beyond wheeled robots to more versatile and temporally-aware systems. To support future research, we will release the codes to our GitHub repo at https://github.com/oskarnatan/Seq-DeepIPC.
