Seq-DeepIPC: Sequential Sensing for End-to-End Control in Legged Robot Navigation

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 EfficientNet-B0 as the encoder, reducing computation while maintaining accuracy. Heading estimation is simplified by removing the noisy IMU and instead computing the bearing angle directly from consecutive GNSS positions. 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.