  The combination of data from multiple sensors, also known as sensor fusion or
data fusion, is a key aspect in the design of autonomous robots. In particular,
algorithms able to accommodate sensor fusion techniques enable increased
accuracy, and are more resilient against the malfunction of individual sensors.
The development of algorithms for autonomous navigation, mapping and
localization have seen big advancements over the past two decades. Nonetheless,
challenges remain in developing robust solutions for accurate localization in
dense urban environments, where the so called last-mile delivery occurs. In
these scenarios, local motion estimation is combined with the matching of
real-time data with a detailed pre-built map. In this paper, we utilize data
gathered with an autonomous delivery robot to compare different sensor fusion
techniques and evaluate which are the algorithms providing the highest accuracy
depending on the environment. The techniques we analyze and propose in this
paper utilize 3D lidar data, inertial data, GNSS data and wheel encoder
readings. We show how lidar scan matching combined with other sensor data can
be used to increase the accuracy of the robot localization and, in consequence,
its navigation. Moreover, we propose a strategy to reduce the impact on
navigation performance when a change in the environment renders map data
invalid or part of the available map is corrupted.
