Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance

TL;DR

This paper introduces a novel indoor navigation framework that uses an implicit obstacle map learned from experience and a non-local memory module to enhance obstacle avoidance robustness and navigation efficiency.

Contribution

It proposes an implicit obstacle map driven by experience and a non-local memory module to improve indoor navigation robustness and efficiency.

Findings

Effective obstacle avoidance demonstrated on AI2-Thor and RoboTHOR benchmarks.

Improved navigation efficiency compared to visual image-based methods.

Robustness against missing or missed obstacle detections.

Abstract

Robust obstacle avoidance is one of the critical steps for successful goal-driven indoor navigation tasks.Due to the obstacle missing in the visual image and the possible missed detection issue, visual image-based obstacle avoidance techniques still suffer from unsatisfactory robustness. To mitigate it, in this paper, we propose a novel implicit obstacle map-driven indoor navigation framework for robust obstacle avoidance, where an implicit obstacle map is learned based on the historical trial-and-error experience rather than the visual image. In order to further improve the navigation efficiency, a non-local target memory aggregation module is designed to leverage a non-local network to model the intrinsic relationship between the target semantic and the target orientation clues during the navigation process so as to mine the most target-correlated object clues for the navigation decision. Extensive experimental results on AI2-Thor and RoboTHOR benchmarks verify the excellent obstacle avoidance and navigation efficiency of our proposed method. The core source code is available at https://github.com/xwaiyy123/object-navigation.