A SoC with FPGA Landmark Acquisition System for Binocular Visual SLAM

Schulz, Victor Hugo; Bombardelli, Felipe Gustavo; Todt, Eduardo

doi:10.1109/lars-sbr.2015.32

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…A more general framework as a real-time version of the SfM technique is the SLAM method [56], where a mapping system starts motion from an unknown location in an unknown environment to estimate its own positions according to the surrounding environment during the movement, while using its own positions to build an incremental map. SLAM is the process of capturing and recovering the geometrical structures of the whole scene by using either active (e.g., laser sensor [57] and depth cameras [58]) or passive (e.g., monocular [59] and binocular stereo [60]) sensing techniques. Figure 5 summarizes the publication statistics related to SLAM, covering the period from 2000 to 2019, after searching for the keywords in Web of Science.…”

Section: Simultaneous Localization and Mapping (Slam)mentioning

confidence: 99%

A Review of Techniques for 3D Reconstruction of Indoor Environments

Zhang

Yang

Zhou

et al. 2020

IJGI

119

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Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand.

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