urban navigation

Recently published datasets have been increasingly comprehensive with respect to their variety of simultaneously used sensors, traffic scenarios, environmental conditions, and provided annotations. However, these datasets typically only consider data collected by one independent vehicle.

There is currently a lack of comprehensive, real-world, multi-vehicle datasets fostering research on cooperative applications such as object detection, urban navigation, or multi-agent SLAM. In this paper, we aim to fill this gap by introducing the novel LUCOOP dataset, which provides time-synchronized multi-modal data collected by three interacting measurement vehicles.

In this contribution, we aim to demonstrate the feasibility of applying the alternative integrity approach to autonomous navigation in terms of several key aspects, i.e., the handling of GNSS multipath effect in the urban environment, fault detection and exclusion, and further consideration of applying weighting models.

For safety critical applications like autonomous driving, high trust in the reported navigation solution is mandatory. This trust can be expressed by the navigation performance parameters, especially integrity. Multipath errors are the most challenging error source in GNSS since only partial correction is possible. In order to ensure high integrity of GNSS-based urban navigation, signal propagation mechanisms and the potential error sources induced by the complex measurement environment should be sufficiently understood.

Talk at ION GNSS+ 2021, awarded best presentation of the track.

Integrity monitoring is of great importance for Global Navigation Satellite Systems (GNSS) applications. Unlike classical approaches …