Real-time smart and standalone vision/IMU navigation sensor

In this paper, we present a smart, standalone, multi-platform stereo vision/IMU-based navigation system, providing ego-motion estimation. The real-time visual odometry algorithm is run on a nano ITX single-board computer (SBC) of 1.9 GHz CPU and 16-core GPU. High-resolution stereo images of 1.2 megapixel provide high-quality data. Tracking of up to 750 features is made possible at 5 fps thanks to a minimal, but efficient, features detection–stereo matching–feature tracking scheme runs on the GPU. Furthermore, the feature tracking algorithm benefits from assistance of a 100 Hz IMU whose accelerometer and gyroscope data provide inertial features prediction enhancing execution speed and tracking efficiency. In a space mission context, we demonstrate robustness and accuracy of the real-time generated 6-degrees-of-freedom trajectories from our visual odometry algorithm. Performance evaluations are comparable to ground truth measurements from an external motion capture system

Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

Stereo vision augmented Medipix3 for real-time material discrimination

Abstract. A secure and sustainable supply of raw materials, such as minerals and metals, is a major challenge for the European Union. Domestic consumption of minerals and metals substantially exceeds production, leading to a high reliance on imports to meet demand, which is driven by the various sectors of the EU’s economy. This thesis was conducted as a part of the Horizon 2020 funded X-MINE project, which aims to address the issue by combining novel sensing technologies to improve resource characterization and economic feasibility of domestic mining operations. The focus of the thesis is within early stage ore extraction, where a real-time sensing platform can be employed in a way to reduce waste at an early stage, decreasing the environmental footprint generated by downstream processing. The advances in CMOS technology have enabled the development of photon counting hybrid pixel detectors, such as Medipix3, for noise-free, high resolution X-ray imaging. Combined with high-speed readout electronics, Medipix3 can be used for imaging and identifying higher density intrusions within ore samples in real-time. In addition, developments in GPU-accelerated stereo vision and related hardware have resulted in high-speed stereo vision camera systems. The combination of stereo vision and Medipix3 is explored in this thesis for real-time material discrimination purposes. In collaboration with multiple European mines, ore samples with reference elemental data were obtained and measured using the combination of stereo vision and Medipix3. In addition, Medipix3 supports a simultaneous dual channel measurement mode, which is used to form a comparison to the performance of the presented system. Measurement principles and physics for both X-ray imaging and stereo vision are discussed. Additionally, calibration methods and algorithms for both measurement modes are presented. Furthermore, methods for data fusion and algorithm performance evaluation are outlined. Results for both measurement modes are presented, along with the relevant measurement physics. Superior performance is obtained with the augmentation of stereo vision, in part due to adverse effects of high-speed imaging on image quality with X-ray imaging. Dual channel approach requires higher data throughput, which results in a reduced integration time in comparison. Additionally, charge sharing effects due to high resolution reduce the spectral measurement capabilities

Real-Time Stereo Vision System: A Multi-Block Matching on GPU

Real-time stereo vision is attractive in many areas such as outdoor mapping and navigation. As a popular accelerator in the image processing field, GPU is widely used for the studies of the stereo vision algorithms. Recently, many stereo vision systems on GPU have achieved low error rate, as a result of the development of deep learning. However, their processing speed is normally far from the real-time requirement. In this paper, we propose a real-time stereo vision system on GPU for the high-resolution images. This system also maintains a low error rate compared with other fast systems. In our approach, the image is resized to reduce the computational complexity and to realize the real-time processing. The low error rate is kept by using the cost aggregation with multiple blocks, secondary matching and sub-pixel estimation. Its processing speed is 41 fps for 2888×1920 pixels images when the maximum disparity is 760

Stereo Event-based Visual-Inertial Odometry

Event-based cameras are new type vision sensors whose pixels work independently and respond asynchronously to brightness change with microsecond resolution, instead of providing standard intensity frames. Compared with traditional cameras, event-based cameras have low latency, no motion blur, and high dynamic range (HDR), which provide possibilities for robots to deal with some challenging scenes. We propose a visual-inertial odometry for stereo event-based cameras based on Error-State Kalman Filter (ESKF). The visual module updates the pose relies on the edge alignment of a semi-dense 3D map to a 2D image, and the IMU module updates pose by median integral. We evaluate our method on public datasets with general 6-DoF motion and compare the results against ground truth. We show that our proposed pipeline provides improved accuracy over the result of the state-of-the-art visual odometry for stereo event-based cameras, while running in real-time on a standard CPU (low-resolution cameras). To the best of our knowledge, this is the first published visual-inertial odometry for stereo event-based cameras

ReS2tAC -- UAV-Borne Real-Time SGM Stereo Optimized for Embedded ARM and CUDA Devices

With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in mind, we propose an approach for real-time embedded stereo processing on ARM and CUDA-enabled devices, which is based on the popular and widely used Semi-Global Matching algorithm. In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs. We have evaluated our approach with different configurations on two public stereo benchmark datasets to demonstrate that they can reach an error rate as low as 3.3%. Furthermore, our experiments show that the fastest configuration of our approach reaches up to 46 FPS on VGA image resolution. Finally, in a use-case specific qualitative evaluation, we have evaluated the power consumption of our approach and deployed it on the DJI Manifold 2-G attached to a DJI Matrix 210v2 RTK unmanned aerial vehicle (UAV), demonstrating its suitability for real-time stereo processing onboard a UAV

ReS²tAC—UAV-borne real-time SGM stereo optimized for embedded ARM and CUDA devices

With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in mind, we propose an approach for real-time embedded stereo processing on ARM and CUDA-enabled devices, which is based on the popular and widely used Semi-Global Matching algorithm. In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs. We have evaluated our approach with different configurations on two public stereo benchmark datasets to demonstrate that they can reach an error rate as low as 3.3%. Furthermore, our experiments show that the fastest configuration of our approach reaches up to 46 FPS on VGA image resolution. Finally, in a use-case specific qualitative evaluation, we have evaluated the power consumption of our approach and deployed it on the DJI Manifold 2-G attached to a DJI Matrix 210v2 RTK unmanned aerial vehicle (UAV), demonstrating its suitability for real-time stereo processing onboard a UAV