  Safe motion planning in robotics requires planning into space which has been
verified to be free of obstacles. However, obtaining such environment
representations using lidars is challenging by virtue of the sparsity of their
depth measurements. We present a learning-aided 3D lidar reconstruction
framework that upsamples sparse lidar depth measurements with the aid of
overlapping camera images so as to generate denser reconstructions with more
definitively free space than can be achieved with the raw lidar measurements
alone. We use a neural network with an encoder-decoder structure to predict
dense depth images along with depth uncertainty estimates which are fused using
a volumetric mapping system. We conduct experiments on real-world outdoor
datasets captured using a handheld sensing device and a legged robot. Using
input data from a 16-beam lidar mapping a building network, our experiments
showed that the amount of estimated free space was increased by more than 40%
with our approach. We also show that our approach trained on a synthetic
dataset generalises well to real-world outdoor scenes without additional
fine-tuning. Finally, we demonstrate how motion planning tasks can benefit from
these denser reconstructions.
