Efficient FPGA Implementation of K-Nearest-Neighbor Search Algorithm for 3D LIDAR Localization and Mapping in Smart Vehicles

Sun, Haofei; Liu, Xinzhe; Deng, Qiudong; Jiang, Weixiong; Luo, Shaobo; Ha, Yajun

doi:10.1109/tcsii.2020.3013758

Cited by 22 publications

(3 citation statements)

References 7 publications

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“…Compared to visual SLAM, only a few studies have considered an FPGA acceleration of LiDAR SLAM despite its importance and widespread use. In [46], a novel voxelbased data structure for hierarchical point cloud partitioning is proposed, and an FPGA-based KNN (k-nearest neighbor) accelerator for 3D point cloud matching is also presented. Though the accelerator provides orders of magnitude faster search speed than CPU counterpart while consuming less energy, its performance effects on 3D LiDAR SLAM are not evaluated.…”

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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“…The set consisting of the class label of each sample in the set can be denoted as L = fC i ji = 1, 2, ⋯, mg. The test sample set is denoted as S = fS i = ðs i1 , s i2 , ⋯s in Þji = 1, 2, ⋯, ag, where a is the number of test samples [16,17]. Then, KNN classification calculation, where a is the number of test samples.…”

Section: Literature Reviewmentioning

confidence: 99%

Multispectral Remote Sensing Data Analysis Based on KNNLC Algorithm and Multimedia Image

Sun¹

2022

Journal of Sensors

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In order to combine multimedia imagery and multispectral remote sensing data to analyze information, preprocessing becomes a necessary part of it. It is found that the KNN algorithm is one of the classic algorithms of data mining. As one of the most important branches in the field of data analysis, it is widely used in many fields such as classification, regression, missing value filling, and machine learning. As a lazy algorithm, this method requires no prior statistical knowledge and no additional data to train description rules and is easy to implement. However, the algorithm inevitably has many problems, such as how to determine the appropriate K value, the unsatisfactory effect of data processing for some special distributions, and the unacceptable computational complexity of high-dimensional data. In order to solve these shortcomings, the researchers proposed the KNNLC algorithm. Then, taking the classification experiment as an example, through the comparison of the experimental results on different data sets, it is proved that the average level of the classification performance of the KNNLC algorithm is better than the classic KNN classification algorithm. The KNNLC algorithm shows better performance in most cases, with an accuracy rate of 2 to 5 percentage points higher. An improved algorithm is proposed for the nearest neighbor selection strategy of the traditional KNN algorithm. First, in theory, combined with the theory of sparse coding and locally constrained linear coding, the classical KNN algorithm is improved, and the KNNLC algorithm is proposed. The comparison of the experimental results on the data set proves that the average level of the KNNLC algorithm is better than the classical KNN classification algorithm in terms of classification performance.

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“…In (Sun et al, 2020) a new data structure with a spatial partitioning method was presented, which can be successfully built even for large volumes of point clouds. Based on this structure, a KNN search algorithm was developed that works effectively when the distribution of points is uneven.…”

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confidence: 99%

Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology

Mochurad

2024

Front. Robot. AI

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Introduction: Navigation satellite systems can fail to work or work incorrectly in a number of conditions: signal shadowing, electromagnetic interference, atmospheric conditions, and technical problems. All of these factors can significantly affect the localization accuracy of autonomous driving systems. This emphasizes the need for other localization technologies, such as Lidar.Methods: The use of the Kalman filter in combination with Lidar can be very effective in various applications due to the synergy of their capabilities. The Kalman filter can improve the accuracy of lidar measurements by taking into account the noise and inaccuracies present in the measurements.Results: In this paper, we propose a parallel Kalman algorithm in three-dimensional space to speed up the computational speed of Lidar localization. At the same time, the initial localization accuracy of the latter is preserved. A distinctive feature of the proposed approach is that the Kalman localization algorithm itself is parallelized, rather than the process of building a map for navigation. The proposed algorithm allows us to obtain the result 3.8 times faster without compromising the localization accuracy, which was 3% for both cases, making it effective for real-time decision-making.Discussion: The reliability of this result is confirmed by a preliminary theoretical estimate of the acceleration rate based on Ambdahl’s law. Accelerating the Kalman filter with CUDA for Lidar localization can be of significant practical value, especially in real-time and in conditions where large amounts of data from Lidar sensors need to be processed.

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Efficient FPGA Implementation of K-Nearest-Neighbor Search Algorithm for 3D LIDAR Localization and Mapping in Smart Vehicles

Cited by 22 publications

References 7 publications

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

Multispectral Remote Sensing Data Analysis Based on KNNLC Algorithm and Multimedia Image

Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology

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