Situational Graphs for Robot Navigation in Structured Indoor Environments

Bavle, Hriday; Sánchez-López, José Luis; Shaheer, Muhammad; Civera, Javier; Voos, Holger

doi:10.48550/arxiv.2202.12197

Cited by 2 publications

(2 citation statements)

References 29 publications

(42 reference statements)

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“…Identifying objects in the camera's field of view-e.g., doors, windows, people, etc.-is a trendy topic in current and future VSLAM works, as the semantic information can be used in pose estimation, trajectory planning, and loop closure detection modules. In this regard, 3D LiDAR-based frameworks, such as Situational Graph (S-Graph) [124], employ planar surfaces and semantic data to illustrate the surroundings face trouble in areas with a high presence of glass. With the widespread usage of object detection and tracking algorithms, semantic VSLAMs will undoubtedly be among the future solutions in this domain.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Visual SLAM: What Are the Current Trends and What to Expect?

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

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In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them.

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Section: Semantic Segmentationmentioning

confidence: 99%

Visual SLAM: What Are the Current Trends and What to Expect?

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…For example, a robot may not need to actively update information about the contents of the inside of buildings while navigating down a street, but once it enters a building, that data becomes relevant. Hierarchical representations, such as the object-based representations of Ok et al [103], the S-graphs of Bavle et al [9], or 3D dynamic scene graphs (e.g., Hughes et al [66] and Rosinol et al [121]) may enable this type of semantically-informed graph compression. Along these lines, it would be interesting to consider whether hybrid factor graph models or the DC-SAM tools developed as part of this thesis may be useful for the development of these technologies.…”

Section: Efficient Inference Compression and Hierarchymentioning

confidence: 99%

Lifelong, learning-augmented robot navigation

Doherty¹

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Simultaneous localization and mapping (SLAM) is the process by which a robot constructs a global model of an environment from local observations of it; this is a fundamental perceptual capability supporting planning, navigation, and control. We are interested in improving the expressiveness and operational longevity of SLAM systems. In particular, we are interested in leveraging state-of-the-art machine learning methods for object detection to augment the maps robots can build with object-level semantic information. To do so, a robot must combine continuous geometric information about its trajectory and object locations with discrete semantic information about object classes. This problem is complicated by the fact that object detection techniques are often unreliable in novel environments, introducing outliers and making it difficult to determine the correspondence between detected objects and mapped landmarks. For robust long-term navigation, a robot must contend with these discrete sources of ambiguity. Finally, even when measurements are not corrupted by outliers, long-term SLAM remains a challenging computational problem: typical solution methods rely on local optimization techniques that require a good “initial guess,” and whose computational expense grows as measurements accumulate. The first contribution of this thesis addresses the problem of inference for hybrid probabilistic models, i.e., models containing both discrete and continuous states we would like to estimate. These problems frequently arise when modeling e.g., outlier contamination (where binary variables indicate whether a measurement is corrupted), or when performing object-level mapping (where discrete variables may represent measurement-landmark correspondence or object categories). The former application is crucial for designing more robust perception systems. The latter application is especially important for enabling robots to construct semantic maps; that is, maps containing objects whose states are a mixture of continuous (geometric) information and (discrete) categorical information (such as class labels). The second contribution of this thesis is, a novel spectral initialization method which is efficient to compute, easy to implement, and admits the first formal performance guarantees for a SLAM initialization method. The final contribution of this thesis aims to curtail the growing computational expense of long-term SLAM. In particular, we propose an efficient algorithm for graph sparsification capable of reducing the computational burden of SLAM methods without significantly degrading SLAM solution quality. Taken together, these contributions improve the robustness and efficiency of robot perception approaches in the lifelong setting.

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Situational Graphs for Robot Navigation in Structured Indoor Environments

Cited by 2 publications

References 29 publications

Visual SLAM: What Are the Current Trends and What to Expect?

Visual SLAM: What Are the Current Trends and What to Expect?

Lifelong, learning-augmented robot navigation

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