FPGA-Based Candidate Scoring Acceleration towards LiDAR Mapping

Frank, J. Howard; Yang, Zhiliu; Liu, Chen

doi:10.1109/metrocad51599.2021.00016

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In [47], the authors proposed an approach similar to this paper. Since CSM becomes a bottleneck part in a state-ofthe-art 2D LiDAR SLAM, Google Cartographer [8], they devised an FPGA-based accelerator for CSM using Xilinx ZCU104, that evaluates correspondence scores (i.e., degree of overlap between LiDAR scan and occupancy grid map) for given candidate solutions.…”

Section: Related Workmentioning

confidence: 99%

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

Sugiura

Matsutani

2022

IEEE Access

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LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) serves as a basis for indoor cleaning, navigation, and many other useful applications in both industry and household. From a series of LiDAR scans, it constructs an accurate, globally consistent model of the environment and estimates a robot position inside it. SLAM is inherently computationally intensive; it is a challenging problem to realize a fast and reliable SLAM system on mobile robots with a limited processing capability. To overcome such hurdles, in this paper, we propose a universal, low-power, and resource-efficient accelerator design for 2D LiDAR SLAM targeting resource-limited FPGAs. As scan matching is at the heart of SLAM, the proposed accelerator consists of dedicated scan matching cores on the programmable logic part, and provides software interfaces to facilitate the use. Our accelerator can be integrated to various SLAM methods including the ROS (Robot Operating System)-based ones, and users can switch to a different method without modifying and re-synthesizing the logic part. We integrate the accelerator into three widelyused methods, i.e., scan matching, particle filter, and graph-based SLAM. We evaluate the design in terms of resource utilization, speed, and quality of output results using real-world datasets. Experiment results on a Pynq-Z2 board demonstrate that our design accelerates scan matching and loop-closure detection tasks by up to 14.84x and 18.92x, yielding 4.67x, 4.00x, and 4.06x overall performance improvement in the above methods, respectively. Our design enables the real-time performance while consuming only 2.4W and maintaining accuracy, which is comparable to the software counterparts and even the state-of-the-art methods.INDEX TERMS SLAM, scan matching, FPGA.

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Section: Related Workmentioning

confidence: 99%

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

Sugiura

Matsutani

2022

IEEE Access

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“…Compared with visual SLAM technology, which is sensitive to initialization and illumination changes and consumes resources to calculate depth information, laser SLAM has a stronger ability to capture environmental information and is more suitable for underground environment. There are four main laser mapping algorithms: filtering-based algorithm Gmapping (Wang et al , 2016; Guan et al , 2017; Balasuriya et al , 2016), Karto (Woo et al , 2021; Wang et al , 2021), Hector (Wei et al , 2019; Bassiri et al , 2018) and the graph optimization algorithm Cartographer (Frank et al , 2021; Sun et al , 2020; Xu et al , 2020). Among the various methods, the graph optimization algorithm (Sun et al , 2020) estimates the location of the observed feature based on the predicted location, and then compares it with the location of the feature point on the map, which is most suitable for the underground space environment with insufficient features.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired mobile robot design and autonomous exploration strategy for underground special space

Wang,

Cong,

et al. 2024

RIA

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Purpose To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot. Design/methodology/approach A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition. Findings According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task. Originality/value Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

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“…It is mainly divided into filter- and graph-based SLAM. There are four main laser SLAM algorithms: Gmapping (Peng et al , 2016; Guan et al , 2017; Balasuriya et al , 2016), Karto (Woo et al , 2021; Wang et al , 2021), Hector (Wei et al , 2019; Bassiri et al , 2018) and Cartographer (Frank et al , 2021; Sun et al , 2020; Xu et al , 2020). Map fusion is a key part of multi-agent SLAM.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot raster map fusion without initial relative position

Wang,

Cong,

et al. 2023

RIA

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Purpose The purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps. Design/methodology/approach A fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion. Findings To verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm. Originality/value This algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.

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FPGA-Based Candidate Scoring Acceleration towards LiDAR Mapping

Cited by 3 publications

References 8 publications

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

Bio-inspired mobile robot design and autonomous exploration strategy for underground special space

Multi-robot raster map fusion without initial relative position

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