1 research outputs found
Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding
While training an end-to-end navigation network in the real world is usually
of high cost, simulation provides a safe and cheap environment in this training
stage. However, training neural network models in simulation brings up the
problem of how to effectively transfer the model from simulation to the real
world (sim-to-real). In this work, we regard the environment representation as
a crucial element in this transfer process and propose a visual information
pyramid (VIP) model to systematically investigate a practical environment
representation. A novel representation composed of spatial and semantic
information synthesis is then established accordingly, where noise model
embedding is particularly considered. To explore the effectiveness of this
representation, we compared the performance with representations popularly used
in the literature in both simulated and real-world scenarios. Results suggest
that our environment representation stands out. Furthermore, an analysis on the
feature map is implemented to investigate the effectiveness through inner
reaction, which could be irradiative for future researches on end-to-end
navigation.Comment: 10 pages, 11 figure