Automated vehicle technology has recently become reliant on 3D LiDAR sensing for perception tasks such as mapping, localization and object detection. This has led to a rapid growth in the LiDAR manufacturing industry with several competing makers releasing new sensors regularly. With this increased variety of LiDARs, each with different properties such as number of laser emitters, resolution, field-of-view, and price tags, a more in-depth comparison of their characteristics and performance is required. This work compares 10 commonly used 3D LiDARs, establishing several metrics to assess their performance. Various outstanding issues with specific LiDARs were qualitatively identified. The accuracy and precision of individual LiDAR beams and accumulated point clouds are evaluated in a controlled environment at distances from 5 to 180 meters. Reflective targets were used to characterize intensity patterns and quantify the impact of surface reflectivity on accuracy and precision. A vehicle and pedestrian mannequin were also used as additional targets of interest. A thorough assessment of these LiDARs is given with their potential applicability for automated driving tasks. The data collected in these experiments and analysis tools are all shared openly.

In this paper, we present a first-of-its-kind method to determine clear and repeatable guidelines for single-shot camera intrinsic calibration using multiple checkerboards. With the help of a simulator, we found the position and rotation intervals that allow optimal corner detector performance. With these intervals defined, we generated thousands of multiple checkerboard poses and evaluated them using ground truth values, in order to obtain configurations that lead to accurate camera intrinsic parameters. We used these results to define guidelines to create multiple checkerboard setups. We tested and verified the robustness of the guidelines in the simulator, and additionally in the real world with cameras with different focal lengths and distortion profiles, which help generalize our findings. Finally, we used a 3D LiDAR (Light Detection and Ranging) to project and confirm the quality of the intrinsic parameters projection. We found it possible to obtain accurate intrinsic parameters for 3D applications, with at least seven checkerboard setups in a single image that follow our positioning guidelines.

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Clustering is the task of dividing an input dataset into groups of objects based on their similarity. This process is frequently required in many applications. However, it is computationally expensive when running on traditional CPUs due to the large number of connections and objects the system needs to inspect. In this paper, we investigate the use of NVIDIA graphics processing units and their programming platform CUDA in the acceleration of the Euclidean clustering (EC) process in autonomous driving systems. We propose GPU-accelerated algorithms for the EC problem on point cloud datasets, optimization strategies, and discuss implementation issues of each method. Our experiments show that our solution outperforms the CPU algorithm with speedup rates up to 87X on real-world datasets.

The three-dimensional (3D) normal distributions transform (NDT) is a popular scan registration method for 3D point cloud datasets. It has been widely used in sensor-based localization and mapping applications. However, the NDT cannot entirely utilize the computing power of modern many-core processors, such as graphics processing units (GPUs), because of the NDT’s linear nature. In this study, we investigated the use of NVIDIA’s GPUs and their programming platform called compute unified device architecture (CUDA) to accelerate the NDT algorithm. We proposed a design and implementation of our GPU-accelerated 3D NDT (GPU NDT). Our methods can achieve a speedup rate of up to 34 times, compared with the NDT implemented in the point cloud library (PCL).