VLPG-Nav: Object Navigation Using Visual Language Pose Graph and Object Localization Probability Maps

TL;DR

VLPG-Nav is a novel robot navigation method that uses visual language pose graphs and object localization probability maps to effectively locate and center objects within household scenes, handling occlusions and localization errors.

Contribution

The paper introduces VLPG-Nav, a new approach combining visual language pose graphs and probability maps for improved object navigation and centering in household environments.

Findings

Outperforms baseline methods in object localization accuracy

Successfully navigates around occlusions and displacement

Effectively centers objects within the camera view in real-world tests

Abstract

We present VLPG-Nav, a visual language navigation method for guiding robots to specified objects within household scenes. Unlike existing methods primarily focused on navigating the robot toward objects, our approach considers the additional challenge of centering the object within the robot's camera view. Our method builds a visual language pose graph (VLPG) that functions as a spatial map of VL embeddings. Given an open vocabulary object query, we plan a viewpoint for object navigation using the VLPG. Despite navigating to the viewpoint, real-world challenges like object occlusion, displacement, and the robot's localization error can prevent visibility. We build an object localization probability map that leverages the robot's current observations and prior VLPG. When the object isn't visible, the probability map is updated and an alternate viewpoint is computed. In addition, we propose an object-centering formulation that locally adjusts the robot's pose to center the object in the camera view. We evaluate the effectiveness of our approach through simulations and real-world experiments, evaluating its ability to successfully view and center the object within the camera field of view. VLPG-Nav demonstrates improved performance in locating the object, navigating around occlusions, and centering the object within the robot's camera view, outperforming the selected baselines in the evaluation metrics.