  Over the last decade, robotic perception algorithms have significantly
benefited from the rapid advances in deep learning (DL). Indeed, a significant
amount of the autonomy stack of different commercial and research platforms
relies on DL for situational awareness, especially vision sensors. This work
explores the potential of general-purpose DL perception algorithms,
specifically detection and segmentation neural networks, for processing
image-like outputs of advanced lidar sensors. Rather than processing the
three-dimensional point cloud data, this is, to the best of our knowledge, the
first work to focus on low-resolution images with 360\textdegree field of view
obtained with lidar sensors by encoding either depth, reflectivity, or
near-infrared light in the image pixels. We show that with adequate
preprocessing, general-purpose DL models can process these images, opening the
door to their usage in environmental conditions where vision sensors present
inherent limitations. We provide both a qualitative and quantitative analysis
of the performance of a variety of neural network architectures. We believe
that using DL models built for visual cameras offers significant advantages due
to the much wider availability and maturity compared to point cloud-based
perception.
