DIOR: A Hardware-Assisted Weather Denoising Solution for LiDAR Point Clouds

The interest in developing and deploying fully autonomous vehicles on our public roads has come to a full swing. Driverless capabilities, widely spread in modern vehicles through advanced driver-assistance systems (ADAS), require highly reliable perception features to navigate the environment, being light detection and ranging (LiDAR) sensors a key instrument in detecting the distance and speed of nearby obstacles and in providing high-resolution 3D representations of the surroundings in real-time. However, and despite being assumed as a game-changer in the autonomous driving paradigm, LiDAR sensors can be very sensitive to adverse weather conditions, which can severely affect the vehicle's perception system behavior. Aiming at improving the LiDAR operation in challenging weather conditions, which contributes to achieving higher driving automation levels defined by the Society of Automotive Engineers (SAE), this article proposes a weather denoising method called Dynamic light-Intensity Outlier Removal (DIOR). DIOR combines two approaches of the state-of-the-art, the dynamic radius outlier removal (DROR) and the low-intensity outlier removal (LIOR) algorithms, supported by an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, DIOR can outperform state-of-the-art outlier removal solutions, achieving better accuracy and performance while guaranteeing the real-time requirements.

Automated summary

The development of fully autonomous vehicles has led to a growing interest in reliable perception features, with LiDAR sensors playing a key role. However, these sensors can be sensitive to adverse weather conditions, affecting the vehicle's perception system behavior. This article proposes a weather denoising method called DIOR, which combines two state-of-the-art algorithms and utilizes FPGA technology to outperform existing solutions, improving accuracy and performance while meeting real-time requirements.