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Register a new userReal-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC
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Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption constrain the feasible hardware to embedded platforms such as FPGAs. Whilst various stereo algorithms have been deployed on these platforms, usually cut down to better match the embedded architecture, certain key parts of the more advanced algorithms, e.g. those that rely on unpredictable access to memory or are highly iterative in nature, are difficult to deploy efficiently on FPGAs, and thus the depth quality that can be achieved is limited. In this paper, we leverage a FPGA-CPU chip to propose a novel, sophisticated, stereo approach that combines the best features of SGM and ELAS-based methods to compute highly accurate dense depth in real time. Our approach achieves an 8.7% error rate on the challenging KITTI 2015 dataset at over 50 FPS, with a power consumption of only 5W.
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This paper presents a field-programmable gate array (FPGA) design of a segmentation algorithm based on convolutional neural network (CNN) that can process light detection and ranging (LiDAR) data in real-time. For autonomous vehicles, drivable region segmentation is an essential step that sets up the static constraints for planning tasks. Traditional drivable region segmentation algorithms are mostly developed on camera data, so their performance is susceptible to the light conditions and the qualities of road markings. LiDAR sensors can obtain the 3D geometry information of the vehicle surroundings with high precision. However, it is a computational challenge to process a large amount of LiDAR data in real-time. In this paper, a convolutional neural network model is proposed and trained to perform semantic segmentation using data from the LiDAR sensor. An efficient hardware architecture is proposed and implemented on an FPGA that can process each LiDAR scan in 17.59 ms, which is much faster than the previous works. Evaluated using Ford and KITTI road detection benchmarks, the proposed solution achieves both high accuracy in performance and real-time processing in speed.
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