3,396 research outputs found
3DS-SLAM: A 3D Object Detection based Semantic SLAM towards Dynamic Indoor Environments
The existence of variable factors within the environment can cause a decline
in camera localization accuracy, as it violates the fundamental assumption of a
static environment in Simultaneous Localization and Mapping (SLAM) algorithms.
Recent semantic SLAM systems towards dynamic environments either rely solely on
2D semantic information, or solely on geometric information, or combine their
results in a loosely integrated manner. In this research paper, we introduce
3DS-SLAM, 3D Semantic SLAM, tailored for dynamic scenes with visual 3D object
detection. The 3DS-SLAM is a tightly-coupled algorithm resolving both semantic
and geometric constraints sequentially. We designed a 3D part-aware hybrid
transformer for point cloud-based object detection to identify dynamic objects.
Subsequently, we propose a dynamic feature filter based on HDBSCAN clustering
to extract objects with significant absolute depth differences. When compared
against ORB-SLAM2, 3DS-SLAM exhibits an average improvement of 98.01% across
the dynamic sequences of the TUM RGB-D dataset. Furthermore, it surpasses the
performance of the other four leading SLAM systems designed for dynamic
environments
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras
Visual scene understanding is an important capability that enables robots to
purposefully act in their environment. In this paper, we propose a novel
approach to object-class segmentation from multiple RGB-D views using deep
learning. We train a deep neural network to predict object-class semantics that
is consistent from several view points in a semi-supervised way. At test time,
the semantics predictions of our network can be fused more consistently in
semantic keyframe maps than predictions of a network trained on individual
views. We base our network architecture on a recent single-view deep learning
approach to RGB and depth fusion for semantic object-class segmentation and
enhance it with multi-scale loss minimization. We obtain the camera trajectory
using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth
annotated frames in order to enforce multi-view consistency during training. At
test time, predictions from multiple views are fused into keyframes. We propose
and analyze several methods for enforcing multi-view consistency during
training and testing. We evaluate the benefit of multi-view consistency
training and demonstrate that pooling of deep features and fusion over multiple
views outperforms single-view baselines on the NYUDv2 benchmark for semantic
segmentation. Our end-to-end trained network achieves state-of-the-art
performance on the NYUDv2 dataset in single-view segmentation as well as
multi-view semantic fusion.Comment: the 2017 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2017
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