StereoNavNet: Learning to Navigate using Stereo Cameras with Auxiliary Occupancy Voxels

Visual navigation has received significant attention recently.

Most of the prior works focus on predicting navigation actions based on semantic features extracted from visual encoders.

However, these approaches often rely on large datasets and exhibit limited generalizability.

In contrast, our approach draws inspiration from traditional navigation planners that operate on geometric representations, such as occupancy maps.

We propose StereoNavNet (SNN), a novel visual navigation approach employing a modular learning framework comprising perception and policy modules.

Within the perception module, we estimate an auxiliary 3D voxel occupancy grid from stereo RGB images and extract geometric features from it.

These features, along with user-defined goals, are utilized by the policy module to predict navigation actions.

Through extensive empirical evaluation, we demonstrate that SNN outperforms baseline approaches in terms of success rates, success weighted by path length, and navigation error.

Furthermore, SNN exhibits better generalizability, characterized by maintaining leading performance when navigating across previously unseen environments.