Robust Object-based SLAM for High-speed Autonomous Navigation

Ok, Kyel; Liu, Katherine; Frey, Kris; How, Jonathan P.; Roy, Nicholas

doi:10.1109/icra.2019.8794344

Cited by 63 publications

(67 citation statements)

References 28 publications

(45 reference statements)

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“…Also, those observations need to ensure massive viewing angle changes between each other. Considering the moving modes of mobile robots, the viewing angle changes in the vertical and pitch directions are limited, which easily causes unobservable problems [ 15 , 16 ].…”

Section: Methodsmentioning

confidence: 99%

“…Jablonsky et al [ 31 ] explored the use of the gravity direction to constrain the ellipsoid. Ok et al [ 16 ] introduced object size to improve mapping. Hosseinzadeh et al [ 32 ] proposed a SLAM system with points, planes, and quadrics.…”

Section: Related Workmentioning

confidence: 99%

“…The motion lacks viewing angle changes in the vertical and pitch directions, which easily causes unobservable problems [ 15 ]. To address the problem, Ok et al [ 16 ] estimated a texture plane based on the feature points on the object surface and introduced an object size prior as new constraints. The algorithm requires the object’s surface to have apparent texture and known object size prior, which limits the application range.…”

Section: Related Workmentioning

confidence: 99%

“…However, the monocular observation model makes the quadrics’ initialization difficult, as it needs to get at least three observations with massive viewing angle changes to converge. Since it is challenging to generate diversified viewing angles under a mobile robot’s planar motion model, unobservable problems [ 16 ] happen easily. Moreover, QuadricSLAM leaves the data association problem unsolved.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

RGB-D Object SLAM Using Quadrics for Indoor Environments

Liao

Wang

et al. 2020

Sensors

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Indoor service robots need to build an object-centric semantic map to understand and execute human instructions. Conventional visual simultaneous localization and mapping (SLAM) systems build a map using geometric features such as points, lines, and planes as landmarks. However, they lack a semantic understanding of the environment. This paper proposes an object-level semantic SLAM algorithm based on RGB-D data, which uses a quadric surface as an object model to compactly represent the object’s position, orientation, and shape. This paper proposes and derives two types of RGB-D camera-quadric observation models: a complete model and a partial model. The complete model combines object detection and point cloud data to estimate a complete ellipsoid in a single RGB-D frame. The partial model is activated when the depth data is severely missing because of illuminations or occlusions, which uses bounding boxes from object detection to constrain objects. Compared with the state-of-the-art quadric SLAM algorithms that use a monocular observation model, the RGB-D observation model reduces the requirements of the observation number and viewing angle changes, which helps improve the accuracy and robustness. This paper introduces a nonparametric pose graph to solve data associations in the back end, and innovatively applies it to the quadric surface model. We thoroughly evaluated the algorithm on two public datasets and an author-collected mobile robot dataset in a home-like environment. We obtained obvious improvements on the localization accuracy and mapping effects compared with two state-of-the-art object SLAM algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RGB-D Object SLAM Using Quadrics for Indoor Environments

Liao

Wang

et al. 2020

Sensors

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“…Instead, Yang et al [8] propose to directly count the number of matched map points on the detected objects as association criteria, yielding a much efficient performance. Grinvald et al [2] propose to measure the similarity between semantic labels and Ok et al [3] propose to leverage the correlation of hue saturation histogram. The major drawback of these methods is that the designed features or descriptors are typically not general or robust enough and can easily cause incorrect associations.…”

Section: A Data Associationmentioning

confidence: 99%

EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

Zhang

Zhu

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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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 stateof-the-art techniques in accuracy and robustness. The source code is available on https://github.com/yanmin-wu/ EAO-SLAM.

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Fast re-OBJ: real-time object re-identification in rigid scenes

Bayraktar

Wang

DelBue

2022

Machine Vision and Applications

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Re-identifying objects in a rigid scene across varying viewpoints (object Re-ID) is a challenging task, in particular when there are similar, even identical objects coexist in the same environment. Discriminative features play no doubt an essential role in addressing this challenge, while for practical deployment, real-time performance is another desired attribute. We therefore propose a novel framework, named Fast re-OBJ, that is able to improve both Re-ID accuracy and processing speed via tight coupling between the instance segmentation module and embedding generation module. The rich object encoding in the instance segmentation backbone is directly shared to the embedding generation module for training a more discriminative representation via a triplet network. Moreover, we create datasets with the segmentation outputs using real-time object detectors to train and evaluate our object embedding module. With extensive experiments, we prove that our proposed Fast re-OBJ improves the object Re-ID accuracy by 5% and the speed is 5× faster compared to the state-of-the-art methods. The code and dataset will be publicly available upon the paper acceptance.

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Robust Object-based SLAM for High-speed Autonomous Navigation

Cited by 63 publications

References 28 publications

RGB-D Object SLAM Using Quadrics for Indoor Environments

RGB-D Object SLAM Using Quadrics for Indoor Environments

EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

Fast re-OBJ: real-time object re-identification in rigid scenes

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