Energy-efficient FPGA-accelerated LiDAR-based SLAM for embedded robotics

Flottmann, Marcel; Eisoldt, Marc; Gaal, Julian; Rothmann, Marc; Tassemeier, Marco; Wiemann, Thomas; Porrmann, Mario

doi:10.1109/icfpt52863.2021.9609934

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Other advances toward VO/SLAM acceleration include LiDAR SLAM accelerators in [36], [37], and [38], as well as accelerators for Iterative Closest Point (ICP) ( [34] and [35]).…”

Section: ) Accelerating Vo/slam Systems On Fpgasmentioning

confidence: 99%

Exploring Sparse Visual Odometry Acceleration With High-Level Synthesis

Iordanou

Riley

et al. 2023

IEEE Access

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Visual Odometry (VO) systems are widely used to determine the position and orientation of a robot or camera in an unknown environment. They are deployed on resource-constrained platforms, such as drones and Virtual Reality (VR) or Augmented Reality (AR) headsets. VO systems harnessing modern System-on-Chip (SoCs) with integrated Field Programmable Gate Array (FPGA) have the potential to improve the overall systems performance. This paper explores the FPGA acceleration of sparse VO kernels using High-level Synthesis (HLS) as this kind of VO system has been designed to use with lowpower SoCs. We show that both computational and data transfer overheads between the processing cores of the CPU of the SoC and the accelerators on the FPGA need to be optimized to obtain better end-to-end performance. This is a result of the additional data movement incurred when using an FPGA accelerator and also because of the sparse computational nature with predictable or random memory access patterns of the kernels involved. However, state-of-the-art HLS tools are not yet able to perform the required optimizations automatically because they usually assume that the kernels to be accelerated have dense computational patterns with regular memory access. In this paper we propose three, potentially generic, methods to reduce the data transfer between the CPU and the customised hardware kernels on the FPGA; these methods are: (a) approximation based on domain-specific knowledge, (b) image compression, and (c) the use of on-the-fly computation. We present a case study of the use of these methods on SVO, a state-of-the-art sparse VO system with a semi-direct front-end. We demonstrate that our proposed methods can reduce data transfer overhead to achieve better end-to-end performance and that they can be applied not only when using standard Xilinx HLS tools but also with other state-of-the-art HLS tools, such as HeteroFlow. Compared to the baseline performance of the original SVO software on an Arm CPU, our proposed methods assist the HLS and HeteroFlow designs to achieve a speedup of 2.4x and 2.14x, respectively, without noticeable accuracy loss. The HLS and HeteroFlow designs also achieve a 1.85x and 1.89x, respectively, improvement in energy efficiency on the SoC system used. Compared to the SVO software baseline running on the Intel Xeon CPU, our proposed methods assist the HLS and HeteroFlow designs to achieve 8.2x and 8.3x improvement in energy efficiency, respectively.

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“…Other advances toward VO/SLAM acceleration include LiDAR SLAM accelerators in [36], [37], and [38], as well as accelerators for Iterative Closest Point (ICP) ( [34] and [35]).…”

Section: ) Accelerating Vo/slam Systems On Fpgasmentioning

confidence: 99%

Exploring Sparse Visual Odometry Acceleration With High-Level Synthesis

Iordanou

Riley

et al. 2023

IEEE Access

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“…In [49] and [50], the authors presented a LiDAR SLAM system mounted with a LiDAR that performs iterative scanto-map registration and updates a 3D TSDF (Truncated Signed Distance Functions) map on the FPGA fabric. They implemented the design using a Trenz UltraSom+ module (Xilinx UltraScale+ MPSoC), resulting in a 7x throughput improvement and 18x energy consumption reduction compared to Intel NUC (Intel Core i7-6770HQ).…”

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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Vision-Guided Robot for Product Inspection

Natarajan,

Megalingam

2023

2023 International Conference on IoT, Communication and Automation Technology (ICICAT)

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Energy-efficient FPGA-accelerated LiDAR-based SLAM for embedded robotics

Cited by 8 publications

References 12 publications

Exploring Sparse Visual Odometry Acceleration With High-Level Synthesis

Exploring Sparse Visual Odometry Acceleration With High-Level Synthesis

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

Vision-Guided Robot for Product Inspection

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