OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM

TL;DR

OA-SLAM introduces an object-based approach to enhance camera relocalization in visual SLAM, combining high-level semantic object landmarks with traditional points to improve robustness and recovery in challenging environments.

Contribution

The paper presents a novel object-aided SLAM system that automatically identifies, tracks, and reconstructs objects, significantly improving relocalization and tracking recovery capabilities.

Findings

Objects provide higher-level semantic cues for relocalization.

Combining points and objects enhances pose recovery in SLAM.

System successfully relocalizes camera in viewpoints where classical methods fail.

Abstract

In this work, we explore the use of objects in Simultaneous Localization and Mapping in unseen worlds and propose an object-aided system (OA-SLAM). More precisely, we show that, compared to low-level points, the major benefit of objects lies in their higher-level semantic and discriminating power. Points, on the contrary, have a better spatial localization accuracy than the generic coarse models used to represent objects (cuboid or ellipsoid). We show that combining points and objects is of great interest to address the problem of camera pose recovery. Our main contributions are: (1) we improve the relocalization ability of a SLAM system using high-level object landmarks; (2) we build an automatic system, capable of identifying, tracking and reconstructing objects with 3D ellipsoids; (3) we show that object-based localization can be used to reinitialize or resume camera tracking. Our fully automatic system allows on-the-fly object mapping and enhanced pose tracking recovery, which we think, can significantly benefit to the AR community. Our experiments show that the camera can be relocalized from viewpoints where classical methods fail. We demonstrate that this localization allows a SLAM system to continue working despite a tracking loss, which can happen frequently with an uninitiated user. Our code and test data are released at gitlab.inria.fr/tangram/oa-slam.