35 research outputs found
Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
Visual simultaneous localization and mapping (SLAM) systems face challenges
in detecting loop closure under the circumstance of large viewpoint changes. In
this paper, we present an object-based loop closure detection method based on
the spatial layout and semanic consistency of the 3D scene graph. Firstly, we
propose an object-level data association approach based on the semantic
information from semantic labels, intersection over union (IoU), object color,
and object embedding. Subsequently, multi-view bundle adjustment with the
associated objects is utilized to jointly optimize the poses of objects and
cameras. We represent the refined objects as a 3D spatial graph with semantics
and topology. Then, we propose a graph matching approach to select
correspondence objects based on the structure layout and semantic property
similarity of vertices' neighbors. Finally, we jointly optimize camera
trajectories and object poses in an object-level pose graph optimization, which
results in a globally consistent map. Experimental results demonstrate that our
proposed data association approach can construct more accurate 3D semantic
maps, and our loop closure method is more robust than point-based and
object-based methods in circumstances with large viewpoint changes
Scale Estimation with Dual Quadrics for Monocular Object SLAM
The scale ambiguity problem is inherently unsolvable to monocular SLAM
without the metric baseline between moving cameras. In this paper, we present a
novel scale estimation approach based on an object-level SLAM system. To obtain
the absolute scale of the reconstructed map, we derive a nonlinear optimization
method to make the scaled dimensions of objects conforming to the distribution
of their sizes in the physical world, without relying on any prior information
of gravity direction. We adopt the dual quadric to represent objects for its
ability to fit objects compactly and accurately. In the proposed monocular
object-level SLAM system, dual quadrics are fastly initialized based on
constraints of 2-D detections and fitted oriented bounding box and are further
optimized to provide reliable dimensions for scale estimation.Comment: 8 pages, 6 figures, accepted by IROS202
OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM
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.Comment: ISMAR 202
EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association
Object-level data association and pose estimation play a fundamental role in
semantic SLAM, which remain unsolved due to the lack of robust and accurate
algorithms. In this work, we propose an ensemble data associate strategy for
integrating the parametric and nonparametric statistic tests. By exploiting the
nature of different statistics, our method can effectively aggregate the
information of different measurements, and thus significantly improve the
robustness and accuracy of data association. We then present an accurate object
pose estimation framework, in which an outliers-robust centroid and scale
estimation algorithm and an object pose initialization algorithm are developed
to help improve the optimality of pose estimation results. Furthermore, we
build a SLAM system that can generate semi-dense or lightweight object-oriented
maps with a monocular camera. Extensive experiments are conducted on three
publicly available datasets and a real scenario. The results show that our
approach significantly outperforms state-of-the-art techniques in accuracy and
robustness. The source code is available on:
https://github.com/yanmin-wu/EAO-SLAM.Comment: Accepted to IROS 2020. Project Page:
https://yanmin-wu.github.io/project/eaoslam/; Code:
https://github.com/yanmin-wu/EAO-SLA