Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation

TL;DR

This paper introduces a panoramic visual navigation system for robots that learns to navigate complex environments without relying on maps or compass data, using imitation learning with RGBD input.

Contribution

It presents a novel end-to-end imitation learning framework that enables mapless, compass-free navigation in large, multi-room environments using panoramic views.

Findings

Requires fewer training examples and less training time

Achieves higher accuracy in reaching goals

Produces shorter, more efficient navigation paths

Abstract

We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, or relative position of the target at runtime. Our end-to-end trained agent uses RGB and depth (RGBD) information and can handle large environments (up to $1031m^2$) across multiple rooms (up to $40$) and generalizes to unseen targets. We show that when compared to several baselines our method (1) requires fewer training examples and less training time, (2) reaches the goal location with higher accuracy, and (3) produces better solutions with shorter paths for long-range navigation tasks.