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 widely-used 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$14.84\times$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18.92\times$ </tex-math></inline-formula> , yielding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.67\times$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.00\times$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.06\times$ </tex-math></inline-formula> 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. 

/pdf/a-universal-lidar-slam-accelerator-system-on-low-cost-fpga-23d7vp0c.pdf

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

Accurate positioning and state estimation of surface vessels are prerequisites to achieving autonomous navigation. Recently, the rapid development of 3D LiDARs has promoted the autonomy of both land and aerial vehicles, which has aroused the interest of researchers in the maritime community accordingly. In this paper, the state estimation schemes based on 3D LiDAR scan matching are explored in depth. Firstly, the iterative closest point (ICP) and normal distribution transformation (NDT) algorithms and their variants are introduced in detail. Besides, ten representative registration algorithms are selected from the variants for comparative analysis. Two types of experiments are designed by utilizing the field test data of an ASV equipped with a 3D LiDAR. Both the accuracy and real-time performance of the selected algorithms are systemically analyzed based on the experimental results. It follows that ICP and Levenberg–Marquardt iterative closest point (LMICP) methods perform well on single-frame experiments, while the voxelized generalized iterative closest point (FastVGICP) and multi-threaded optimization generalized iterative closest point (FastGICP) methods have the best performance on continuous-frame experiments. However, all methods have lower accuracy during fast turning. Consequently, the limitations of current methods are discussed in detail, which provides insights for future exploration of accurate state estimation based on 3D LiDAR for ASVs.

/pdf/comparative-analysis-of-3d-lidar-scan-matching-methods-for-ccv33mo9.pdf

Comparative Analysis of 3D LiDAR Scan-Matching Methods for State Estimation of Autonomous Surface Vessel

Feature extraction is a fundamental and essential step in light detection and ranging (LiDAR) based simultaneously localization and mapping (SLAM) algorithms. Considering the run-time requirement of feature extraction and the stringent battery constraint in smart vehicles, it is a great challenge to develop fast and highly energy-efficient feature extraction implementation for massive point clouds. Unfortunately, existing implementations not only fail to exploit the available parallelism but also fail to make full use of the local information to optimize the computations. To solve the issue, we propose three novel techniques to achieve a fast and energy-efficient FPGA implementation of the feature extraction algorithm with effective local search. First, we propose a low-complexity projection method and a column-scanning scheduler to organize the irregular and sparse point cloud into a well-organized point cloud matrix. Second, based on the point cloud matrix, we exploit its local information and propose a high-parallel method to detect the coarse-grain feature points. Third, we propose a high-parallel conditional priority queue to progressively and evenly select the fine-grain feature points. Experimental results on the KITTI dataset show that our method implemented on the ZCU104 FPGA board achieves the best accuracy and reaches 584 frames per second (FPS) for the feature extraction of a 64-laser LiDAR’s point cloud. Moreover, our proposal achieves the best energy efficiency, which is on average 11.7 times and 9.0 times higher than the state-of-the-art implementations on the GPU and FPGA platforms, respectively. 

An Energy-Efficient Stream-Based FPGA Implementation of Feature Extraction Algorithm for LiDAR Point Clouds With Effective Local-Search

In this brief, the encoding-decoding-based distributed filtering problem is studied for a class of time-varying saturated systems with constrained bit rate. The constrained bit rate model is adopted to reflect the bandwidth allocation rules of the digital channel. In order to improve the communication reliability, the encoding-decoding mechanism is used to transmit the data signal under the constrained bit rate. The purpose of this brief is to design a distributed filter based on the measurements after the encoding-decoding procedure such that an upper bound (UB) of the filtering error covariance (FEC) is recursively presented through solving the matrix difference equations. Then, the distributed filter gain is parameterized by minimizing the trace of the proposed UB matrix. Furthermore, the boundedness of the UB of the FEC is ensured. Finally, a numerical simulation is provided to demonstrate the validity of the developed distributed filtering strategy. 

Encoding-Decoding-Based Distributed Filtering for Time-Varying Saturated Systems With Constrained Bit Rate

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.

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

Light detection and ranging (LiDAR) based normal distribution transform (NDT) is a popular localization algorithm in smart vehicles for its robustness and accuracy. To meet the requirement of higher speed of smart vehicles, LiDAR point clouds become increasingly large due to the increase of not only the number of laser channels but also their frame rates. However, previous NDT implementations fail to achieve real-time performance when handling large point clouds because they require an enormous number of search operations and perform intensive computations per search operation. To address this issue, we propose an optimized NDT algorithm and its FPGA implementation. First, we effectively speed up each search operation by proposing a new non-recursive and memory-efficient data structure, called occupation-aware-voxel-structure (OAVS). Second, we significantly reduce the number of search operations by removing redundant searches with an optimized semantic-assisted OAVS-based NDT algorithm (SEO-NDT). Third, we further improve the real-time and energy efficiency by proposing a streaming FPGA accelerator architecture for SEO-NDT. Compared with the state-of-the-art embedded CPU and GPU implementations, our FPGA implementation achieves up to 35.85x and 2.44x execution time speed up, respectively.

An Optimized FPGA-Based Real-Time NDT for 3D-LiDAR Localization in Smart Vehicles

Graph cut has been a popular approach widely used to solve the minimum cut problem, which is prevalent in computer vision tasks, although not limited to this field. Push-relabel is considered as one of the promising algorithms of graph cut due to its good potential to be parallelized. However, existing implementations often not only fail to fully exploit the available parallelism but also fail to make full use of the application context to reduce redundant computations. Therefore, they are not competent for application scenarios with high resolution and real-time requirements. To address the issue, we propose three novel techniques to achieve an ultra-fast and efficient FPGA implementation of a push-relabel algorithm. First, we propose a ripple push technique that significantly parallelizes push operations so as to accelerate the push-relabel convergence process. Second, we propose an early-termination technique that effectively removes redundant computations of the push-relabel algorithm. We also theoretically prove the correctness of our early-termination technique. Third, we propose a highly parallelized search technique called flood irrigation search (FIS). It quickly judges early termination conditions based on a pixel parallel architecture. Our implementation focuses on performance-sensitive applications that divide large images into small graph tiles with a specific size. Compared to the state-of-the-art FPGA implementations of push-relabel algorithms, experimental results show that our method can at least achieve $8.93\times $ improvement of execution time.

Ultra-Fast FPGA Implementation of Graph Cut Algorithm With Ripple Push and Early Termination

Graph cut is a popular approach to solving optimization tasks related to Min-cut/Max-flow problems. However, existing FPGA accelerators of graph cut have difficulty in handling large grid graphs and achieving real-time performance. To address the issue, we propose a novel folding grid architecture that maps an actual one-layered large 2-dimension grid graph into a virtual multi-layered small 2-dimension grid graph. The new architecture not only enables the virtual multi-layered grid graph to execute on a small-size processor array but also adds the potential to concurrently execute grid graph nodes in different layers. In addition, we also propose a novel out-of-order parallel execution technique to fully utilize the architecture parallelism potential. Compared to the state-of-the-art, experimental results show that our design can solve the graph cut problem for grid graphs of 1920 × 1080 nodes in real-time (above 60fps) and achieve a 5.4× improvement in execution time with similar FPGA resources.

Fast FPGA Accelerator of Graph Cut Algorithm with Out-of-order Parallel Execution in Folding Grid Architecture

Fast FPGA Accelerator of Graph Cut Algorithm With Threshold Global Relabel and Inertial Push

Fast FPGA Accelerator of Graph Cut Algorithm based on Cached Folding Grid Architecture

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hui Wang

Author Tools

Chat about Author