The broader impact/commercial potential of this project is the practical deployment of a low-cost and low-power real-time perception system in self-driving consumer cars. This edge computing functionality in sensors enables higher reliability and lower cost of overall sensing and computing needed for truly autonomous self-driving. Such innovation will contribute significantly to the early and widespread availability of safety and convenience benefits to consumers. Furthermore, the advanced perception system will have a potential long-term impact on robotics in general, which can lead to creation of new markets and new lifestyles.This Small Business Innovation Research (SBIR) Phase I project aims to develop efficient algorithms and software implementation of a real-time perception system to enable the use of low-cost computing systems for self-driving cars. The algorithms provide a novel way of using image features to perform simultaneous localization and mapping (SLAM) with 100 times less computational costs than the existing algorithms. They also include a truly novel neural network to fuse the image feature and light detection and ranging (LiDAR) features and perform object detection, which has 100 times less complexity compared to the state-of-the-art method. These reduced-complexity algorithms can be implemented on low-power and low-cost SoC processors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
