Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
This work addresses the need for efficient depth perception in space and mobile robotics by providing a significant speed and power improvement over existing methods, though it is incremental as it adapts an existing algorithm.
The authors tackled the problem of real-time dense stereo matching for low-power robotics by implementing an FPGA-accelerated version of the ELAS algorithm, achieving a frame rate of 47fps with under 4W power consumption compared to 1.5-3 fps on a high-end CPU.
For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be estimated through data-intensive processes. Therefore, appropriate algorithm selection plays an important role in achieving the desired performance characteristics. Motivated by applications in space and mobile robotics, we implement and evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering one of the best trade-offs between efficiency and accuracy, ELAS has only been shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all intriguing properties of the original algorithm, such as the slanted plane priors, but can achieve a frame rate of 47fps whilst consuming under 4W of power. Unlike previous FPGA based designs, we take advantage of both components on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate more complex and computationally diverse algorithms for such low power, real-time systems.