Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAM

Abstract

This paper discusses a novel embedded system-on-chip 3D localization and mapping (eSoC-LAM) implementation, that followed a co-design approach with the primary aim of being deployed in a small system on a programmable chip (SoPC), the Intel’s (a.k.a Altera) Cyclone V 5CSEMA5F31C6N, available in the Terasic’s board DE1-SoC. This computer board incorporates an 800 MHz Dual-core ARM Cortex-A9 and a Cyclone V FPGA with 85k programmable logic elements and 4450 Kbits of embedded memory running at 50 MHz. We report experiments of the eSoC-LAM implementation using a Robosense’s 3D LiDAR RS-16 sensor in a Robotis’ TurtleBot2 differential robot, both controlled by a Terasic’s board DE1-SoC. This paper presents a comprehensive description of the designed architecture, design constraints, resource optimization, HPS-FPGA exchange of information, and co-design results. The eSoC-LAM implementation reached an average speed-up of 6.5× when compared with a version of the algorithm running in a the hard processor system of the Cyclone V device, and a performance of nearly 32 fps, while keeping high map accuracy.

GPU-Accelerated Interaction-Aware Motion Prediction

“…One of the most popular embedded architectures used is field programmable gate arrays (FPGAs) due to their low latency, efficient power consumption and robustness to adverse conditions. In [6], the authors argue that FPGA development is mature enough to host expensive simultaneous localization and mapping algorithms. In [7], they apply an FPGA to offload algorithm components while acting as a fallback system in the case of failure.…”

SLAM Mapping Method of Laser Radar for Tobacco Production Line Inspection Robot Based on Improved RBPF

“…In response to the above problems, many scholars have carried out a lot of effective research, such as the map centered SLAM mapping method of intensive 3D laser anti-radar for tobacco production line patrol robots proposed by Park et al [6], the active SLAM new method of tobacco production line patrol robots using laser radar to draw maps proposed by Malobick et al [7], low delay laser radar SLAM for tobacco production line patrol robot using continuous scanning slices proposed by Karimi et al [8], active SLAM method for tobacco production line patrol robot based on convolutional neural network proposed by Bol et al [9], SLAM method for tobacco production line patrol robot based on embedded system on chip 3D positioning and mapping researched by Gerlein et al [10]. Park et al, in their research on the laser radar SLAM mapping method of the tobacco production line inspection robot, took the map as the center, overcome the shortcomings such as the laser radar motion distortion through the local continuous time trajectory, and used the surface resolution maintenance matching algorithm and the surface fusion model based on the normal inverse Wisart to achieve non redundant but dense mapping.…”