HOOFR SLAM System: An Embedded Vision SLAM Algorithm and Its Hardware-Software Mapping-Based Intelligent Vehicles Applications

Nguyen, Dai-Duong; Elouardi, Abdelhafid; Rodríguez, Sergio; Bouaziz, Samir

doi:10.1109/tits.2018.2881556

Cited by 31 publications

(12 citation statements)

References 32 publications

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“…Authors in [8] developed a novel original featurebased stereo VSLAM framework named HOOFR SLAM based on an enhanced bio-inspired feature extractor Hessian ORB -Overlapped FREAK (HOOFR), which is a combination of FAST detector including Hessian score and amended FREAK bioinspired descriptor. Moreover, they attained to implement it on heterogeneous architecture CPU-GPU.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, the ultimate goal of this paper is improving the performance of our targeted VSLAM algorithm in terms of computational cost. For this purpose, our hardware-software co-design study adopts aforementioned works [8,10], where they provide appealing results tackling the computational complexity of SLAM system, furthermore, they worked on heterogeneous systems porting compute-intensive parts of SLAM system precisely the scan matching process into accelerators: The first [8] paves the way to us toward our targeted ORB-SLAM algorithm that shows reliable results than their proposal, except we will be satisfied with the monocular version. The second [10] leads us toward an efficient trend of heterogenous architectures CPU-FPGA.…”

Section: Related Workmentioning

confidence: 99%

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ORB-SLAM accelerated on heterogeneous parallel architectures

Mamri¹,

Mohamed²,

Ramzi³

et al. 2021

E3S Web Conf.

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SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did not adopt any complete end-to-end hardware implementation yet. Our work aims to a hardware/software optimization of an expensive computational time functional block of monocular ORB-SLAM2. Through this, we attempt to implement the proposed optimization in FPGA-based heterogeneous embedded architecture that shows attractive results. Toward this, we adopt a comparative study with other heterogeneous architecture including powerful embedded GPGPU (NVIDIA Tegra TX1) and high-end GPU (NVIDIA GeForce 920MX). The implementation is achieved using high-level synthesis-based OpenCL for FPGA and CUDA for NVIDIA targeted boards.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

ORB-SLAM accelerated on heterogeneous parallel architectures

Mamri¹,

Mohamed²,

Ramzi³

et al. 2021

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Nowadays, considerable research contributions have been made toward the evolution of visual Simultaneous Localization and Mapping (SLAM) [ 1 , 2 , 3 ]. SLAM plays an essential role in robot applications, intelligent cars, and unmanned aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

LFM: A Lightweight LCD Algorithm Based on Feature Matching between Similar Key Frames

Zhu

Liu

et al. 2021

Sensors

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Loop Closure Detection (LCD) is an important technique to improve the accuracy of Simultaneous Localization and Mapping (SLAM). In this paper, we propose an LCD algorithm based on binary classification for feature matching between similar images with deep learning, which greatly improves the accuracy of LCD algorithm. Meanwhile, a novel lightweight convolutional neural network (CNN) is proposed and applied to the target detection task of key frames. On this basis, the key frames are binary classified according to their labels. Finally, similar frames are input into the improved lightweight feature matching network based on Transformer to judge whether the current position is loop closure. The experimental results show that, compared with the traditional method, LFM-LCD has higher accuracy and recall rate in the LCD task of indoor SLAM while ensuring the number of parameters and calculation amount. The research in this paper provides a new direction for LCD of robotic SLAM, which will be further improved with the development of deep learning.

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“…These lines are then matched with 3D line segments in a CAD model of the environment using a robust matching algorithm. Although this approach brings in numerous innovative features, it does require the robot to be moving only in Special Euclidean group (SE (2)). However, moving in SE(3) would exponentially increase the search space for the initial localization of the robot.…”

Section: Introductionmentioning

confidence: 99%

UAV Autonomous Localization Using Macro-Features Matching with a CAD Model

Haque

Elsaharti

Elderini

et al. 2020

Sensors

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Research in the field of autonomous Unmanned Aerial Vehicles (UAVs) has significantly advanced in recent years, mainly due to their relevance in a large variety of commercial, industrial, and military applications. However, UAV navigation in GPS-denied environments continues to be a challenging problem that has been tackled in recent research through sensor-based approaches. This paper presents a novel offline, portable, real-time in-door UAV localization technique that relies on macro-feature detection and matching. The proposed system leverages the support of machine learning, traditional computer vision techniques, and pre-existing knowledge of the environment. The main contribution of this work is the real-time creation of a macro-feature description vector from the UAV captured images which are simultaneously matched with an offline pre-existing vector from a Computer-Aided Design (CAD) model. This results in a quick UAV localization within the CAD model. The effectiveness and accuracy of the proposed system were evaluated through simulations and experimental prototype implementation. Final results reveal the algorithm's low computational burden as well as its ease of deployment in GPS-denied environments.

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HOOFR SLAM System: An Embedded Vision SLAM Algorithm and Its Hardware-Software Mapping-Based Intelligent Vehicles Applications

Cited by 31 publications

References 32 publications

ORB-SLAM accelerated on heterogeneous parallel architectures

ORB-SLAM accelerated on heterogeneous parallel architectures

LFM: A Lightweight LCD Algorithm Based on Feature Matching between Similar Key Frames

UAV Autonomous Localization Using Macro-Features Matching with a CAD Model

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