Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This article introduces gnssFGO, a global and online trajectory estimator that fuses global navigation satellite systems (GNSS) observations alongside multiple sensor measurements for robust vehicle localization. In gnssFGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process (GP) regression. This enables querying states at arbitrary timestamps without strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multisensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, inertial measurement unit, and LiDAR-odometry. We employed datasets from measurement campaigns in Aachen, Düsseldorf, and Cologne and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas where a classic multisensor fusion method fails due to sensor degradation. In a test sequence containing a 17-km route through Aachen, the proposed method results in a mean 2-D positioning error 0.48 m while fusing raw GNSS observations with LiDAR odometry in a tight coupling.

When tackling problems, designers might be inspired by different sources, whether concrete or abstract. The more concrete sources often comprise representations of potential solutions or examples of existing designs. The more abstract sources often represent the desirable properties of engineered systems, such as modular system architectures. We performed an experiment with 60 novice designers to compare the inspiration effects from these two types of stimuli. Participants were asked to solve a design problem, having been exposed to a concrete example design, an abstract system property, both, or no stimulus at all. Their design work was assessed according to four metrics: fluency, diversity, commonness, and conformity. Exposure to either the example design or the system property reduced the fluency and diversity of ideas, and exposure to both stimuli reduced these measures even further. While there was no difference in the inspiration effects from the example and the property in terms of fluency, diversity, and commonness; results for conformity showed that each stimulus constrained participants differently: encouraging ideas similar to one type of stimulus, while discouraging ideas similar to the other type. In combination with other work on inspiration and fixation, these findings can help shape how design is taught and how inspiration tools are developed.