Multi Sensor Fusion for Navigation and Mapping in Autonomous Vehicles: Accurate Localization in Urban Environments

TL;DR

This paper evaluates sensor fusion techniques combining lidar, inertial, GNSS, and wheel data to improve localization accuracy for autonomous delivery robots in urban environments, addressing challenges of map invalidity and sensor failures.

Contribution

It compares different sensor fusion algorithms and proposes a strategy to maintain localization accuracy despite environmental changes and map corruption.

Findings

Lidar scan matching enhances localization accuracy when combined with other sensors.

Sensor fusion improves robustness against sensor malfunctions.

Proposed strategy mitigates navigation issues caused by map invalidity.

Abstract

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.