Evaluating a Visual Simultaneous Localization and Mapping Solution on Embedded Platforms

Silveira, Onias Castelo Branco; Melo, Joao G O C de; Moreira, Leandro Arantes; Pinto, João Bacelar Nascimento Gomes; Rodrigues, Luiz; Rosa, Paulo Fernando Ferreira

doi:10.1109/isie45063.2020.9152370

Cited by 9 publications

(10 citation statements)

References 13 publications

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“…Furthermore, it was observed that the Jetson Nano's consumption of power was considerably higher when utilizing the TensorFlow-GPU framework as opposed to TensorFlow-RT, despite similar inference times. In a related study, Silveira et al [18] benchmarked the Nvidia Jetson Nano against the RPI 3B+, employing the simultaneous localization and mapping (SLAM) algorithm. Their findings highlighted the nano's superior performance, achieving 12.6 frames per second (FPS) and 12.1 FPS across two datasets, in stark contrast to the RPi 3B+'s 4.4 FPS and 3.6 FPS, respectively.…”

Section: Iot Edge Devicesmentioning

confidence: 99%

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Mahbub,

Bhagwagar,

Chand

et al. 2024

Sensors

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Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.

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Section: Iot Edge Devicesmentioning

confidence: 99%

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Mahbub,

Bhagwagar,

Chand

et al. 2024

Sensors

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“…Figure 16 seeks to illustrate the main differences between the ORB-SLAM algorithm (see Figure 9) and its visual-inertial version. The VIORB algorithm was the first visual-inertial method to employ map reuse, and it presents high-performance accuracy [64,70,72] and memory usage [18]. Nonetheless, the IMU initialization takes between 10 to 15 s [71], and no embedded implementations were found.…”

Section: Visual Inertial Orb-slam (2017)mentioning

confidence: 99%

A Comprehensive Survey of Visual SLAM Algorithms

et al. 2022

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Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown environment. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. The literature presents different approaches and methods to implement visual-based SLAM systems. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. Furthermore, we propose six criteria that ease the SLAM algorithm’s analysis and consider both the software and hardware levels. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques’ main elements and characteristics.

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“…Partitioning a complete localization method with loop closures requires high computing resources. In [155], ORB-SLAM2 [82], which integrates a loop closure module has been optimized using NEON instructions to take advantage of the advanced SIMD used in the ARM processors of the Raspberry Pi 3B+ and Jetson Nano. It achieves an average tracking time of 6.11 FPS on Raspberry Pi 3B+ and 9.64 FPS on Jetson Nano with input images at a 752×480 resolution.…”

Section: A Embedded Platforms For Localization and 3d Reconstructionmentioning

confidence: 99%

“…), poses (25ms), loop (120ms) i7-6820HK 2.7GHz, GeForce GTX 980 M (20 FPS) on Jetson TX2 (28 FPS)[154] [82] on Pi 3B+ (6 FPS), Jetson Nano (10 FPS)[155] CPU (19 FPS global opti., 128 FPS local opti.) 3173 (10 FPS visual-SLAM, 20 FPS lidar-SLAM) : UP Board (7 FPS), ODROID XU4 (7 FPS)[159] : UP Board (20 FPS), ODROID XU4 (20 FPS)[159] RAM, quadcore CPU, Tegra K1 GPU Tango mobile phone, 2GB RAM, quadcore CPU ElasticFusion 47 FPS), iPad Air 2 (21 FPS) DE5 PCIe board (44 FPS), DE1 FPGA SoC (2 FPS)[172] …”

mentioning

confidence: 99%

Work-in-Progress: Smart data reduction in SLAM methods for embedded systems

Picard

Chevobbe

Darouich

et al. 2022

2022 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)

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The 3D reconstruction of simultaneous localization and mapping (SLAM) is an important topic in the field for transport systems such as drones, service robots and mobile AR/VR devices. Compared to a point cloud representation, the 3D reconstruction based on meshes and voxels is particularly useful for high-level functions, like obstacle avoidance or interaction with the physical environment. This article reviews the implementation of a visual-based 3D scene reconstruction pipeline on resource-constrained hardware platforms. Real-time performances, memory management and low power consumption are critical for embedded systems. A conventional SLAM pipeline from sensors to 3D reconstruction is described, including the potential use of deep learning. The implementation of advanced functions with limited resources is detailed. Recent systems propose the embedded implementation of 3D reconstruction methods with different granularities. The trade-off between required accuracy and resource consumption for real-time localization and reconstruction is one of the open research questions identified and discussed in this paper.

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Evaluating a Visual Simultaneous Localization and Mapping Solution on Embedded Platforms

Cited by 9 publications

References 13 publications

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

A Comprehensive Survey of Visual SLAM Algorithms

Work-in-Progress: Smart data reduction in SLAM methods for embedded systems

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