LiDAR technology is gaining popularity for use in 3D object detection, necessary for self-driving cars. However, due to class imbalances in state-of-the-art LiDAR datasets, detection algorithms often tend to lack performance in detecting cyclists. To address this issue, we introduce the \textit{SenseBike}, a LiDAR-equipped bicycle suited for collecting novel data, including more cyclists. We have created the \textit{SenseBike dataset}, which features distinctive data from the city of Delft, The Netherlands. Recording from a bicycle brings unique challenges, and we explain and evaluate our solutions to these issues.
To evaluate the impact of this new dataset on the performance of LiDAR object detection, we adapted an existing pseudo-labeling pipeline. Despite the recommendation, we did not self-train this pipeline, which would have resulted in higher quality pseudo-labels. Nonetheless, when we train CenterPoint, a well-known and fast 3D LiDAR object detector, on these lower-quality pseudo-labels, we still achieve an 85% Average Precision for cyclists, evaluated with maximum center-distance differences of 1m.