Unmanned aerial vehicles (UAVs) are becoming increasingly ubiquitous in our daily lives. However, like many other complex systems, UAVs are susceptible to software bugs that can lead to abnormal system behaviors and undesirable consequences. It is crucial to study such software bug-induced UAV anomalies, which are often manifested in flight logs, to help assure the quality and safety of UAV systems. However, there has been limited research on investigating the code-level patterns of software bug-induced UAV anomalies. This impedes the development of effective tools for diagnosing and localizing bugs within UAV system code. To bridge the research gap and deepen our understanding of UAV anomalies, we carried out an empirical study on this subject. We first collected 178 real-world abnormal logs induced by software bugs in two popular open-source UAV platforms, i.e., PX4 and Ardupilot. We then examined each of these abnormal logs and compiled their common patterns. In particular, we investigated the most severe anomalies, that led to UAV crashes, and identified their features. Based on our empirical findings, we further summarized the challenges of localizing bugs in system code by analyzing anomalous UAV flight data, which can offer insights for future research in this field.