Virtual ADA Compliance Assessment: Mimicking Digital Inclinometers to Measure Slopes within Point Clouds

The Americans with Disabilities Act (ADA) requires that the design, construction, and maintenance of curb ramps provide accessibility to persons with disabilities. Light detection and ranging (lidar) can efficiently collect high-accuracy and high-resolution three-dimensional (3D) geometric data, which can be leveraged for ADA compliance assessment in a virtual environment to help guide and improve field practice. Most existing studies do not consider many aspects of the field inspection process including local variations in slope measurements and the physical instrument characteristics, warranting a more detailed accuracy evaluation. Accordingly, a workflow for virtual ADA compliance inspection of curb ramps using lidar data is proposed and was applied to a controlled experiment using full-size ramp specimens to optimize parameters and compare methods with rigorous accuracy assessments. The proposed workflow consists of data preprocessing, digital elevation model generation, and digital inclinometer simulation. In the digital inclinometer simulation, we developed and implemented four different approaches including surface normal, immediate neighbor linear regression, scaled neighbor linear regression, and touching point extraction. Among these approaches, the novel touching point extraction method considers both the roughness of the surface and the scale in which a digital inclinometer measures the slope. The accuracy and effectiveness of the framework were demonstrated, resulting in a root-mean square error of 0.18% for the mean slope, which is on par with the accuracy of a digital inclinometer (0.2%, $1-\sigma$). An example virtual experiment is provided using the proposed approach to identify the optimal spacing of measurement samples to determine the slope of a curb ramp for ADA compliance assessment.