L-AVATeD: the LiDAR And Visual wAlking Terrain Dataset

An understanding of local walking context plays an important role in the analysis of gait in humans. Laboratory analysis on its own can constrain the ability of researchers to properly assess clinical gait in patients and therefore study in diverse walking environments is warranted. While gait characteristics themselves are measured using a variety of sensors (e.g. Inertial Measurement Units), a ground-truth understanding of the walking terrain (necessary for the interpretation of IMU data) is traditionally identified from simple visual data. Deep Neural Networks, and in particular Convolutional Neural Networks are ideal for this classification task, but require extensive training data. Modern mobile devices include a suite of sensors capable of gathering not only images of walking terrain, but also depth data using built-in LiDAR sensors, and classifiers incorporating this multimodal data can outperform simple visual classification. We therefore present L-AVATeD: the Lidar And Visible wAlking Terrain Dataset, consisting of ~8,000 pairs of visual (RGB) and Depth (LiDAR) data. The data are divided into 9 classes of walking terrain typical of the built environments inside and surrounding North American academic and health-related institutions.