Mobile robots moving in crowded environments have to navigate among pedestrians safely. Ideally, the way the robot avoids the pedestrians should not only be physically safe but also perceived safe and comfortable. Despite the rich literature in collision-free crowd navigation, limited research has been conducted on how humans perceive robot behaviors in the navigation context. In this paper, we implement three local pedestrian avoidance strategies inspired by human avoidance behaviors on a self-balancing mobile robot and evaluate their perception in a human-robot crossing scenario through a large-scale user study with 98 participants. The study reveals that the avoidance strategies positively affect the participants' perception of the robot's safety, comfort, and awareness to different degrees. Furthermore, the participants perceive the robot as more intelligent, friendly and reliable in the last trial than in the first even with the same strategy.