  Autonomous robots that assist humans in day to day living tasks are becoming
increasingly popular. Autonomous mobile robots operate by sensing and
perceiving their surrounding environment to make accurate driving decisions. A
combination of several different sensors such as LiDAR, radar, ultrasound
sensors and cameras are utilized to sense the surrounding environment of
autonomous vehicles. These heterogeneous sensors simultaneously capture various
physical attributes of the environment. Such multimodality and redundancy of
sensing need to be positively utilized for reliable and consistent perception
of the environment through sensor data fusion. However, these multimodal sensor
data streams are different from each other in many ways, such as temporal and
spatial resolution, data format, and geometric alignment. For the subsequent
perception algorithms to utilize the diversity offered by multimodal sensing,
the data streams need to be spatially, geometrically and temporally aligned
with each other. In this paper, we address the problem of fusing the outputs of
a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image
sensor for free space detection. The outputs of LiDAR scanner and the image
sensor are of different spatial resolutions and need to be aligned with each
other. A geometrical model is used to spatially align the two sensor outputs,
followed by a Gaussian Process (GP) regression-based resolution matching
algorithm to interpolate the missing data with quantifiable uncertainty. The
results indicate that the proposed sensor data fusion framework significantly
aids the subsequent perception steps, as illustrated by the performance
improvement of a uncertainty aware free space detection algorithm
