BEyond observation: an approach for ObjectNav

TL;DR

This paper explores sensor data fusion and machine learning for Visual Semantic Navigation, enabling autonomous object-directed navigation without prior environment knowledge, achieving competitive results in the Habitat Challenge 2021.

Contribution

It introduces an approach combining sensor fusion and machine learning for ObjectNav, advancing autonomous navigation without prior environment maps.

Findings

Achieved 4th place in Habitat Challenge 2021 ObjectNav.

Demonstrated effective sensor data fusion for navigation tasks.

Validated approach on standard benchmarks.

Abstract

With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic. It composes a multi-disciplinary field of robotics that encompasses embedded systems, control theory, path planning, Simultaneous Localization and Mapping (SLAM), scene reconstruction, and pattern recognition. In this work, we present our exploratory research of how sensor data fusion and state-of-the-art machine learning algorithms can perform the Embodied Artificial Intelligence (E-AI) task called Visual Semantic Navigation. This task, a.k.a Object-Goal Navigation (ObjectNav) consists of autonomous navigation using egocentric visual observations to reach an object belonging to the target semantic class without prior knowledge of the environment. Our method reached fourth place on the Habitat Challenge 2021 ObjectNav on the Minival phase and the Test-Standard Phase.