It is becoming increasingly important to accurately detect a user's presence at certain locations during certain times of the day, e.g., to study the user's patterns with respect to mobility, behavior, or social interactions and to enable the delivery of targeted services. However, instead of geographic locations, it is often more important to determine a locale that is important to the user, e.g., the place of work, home, homes of family and friends, social gathering places, etc. These significant personal places can be determined through analysis, e.g., via segmentation of location traces into a discrete sequence of places. However, segmentation of traces with many gaps (e.g., due to loss of network or GPS signal) results in a large number of small segments, where many of these segments actually belong together. This work proposes a new segmentation approach that opportunistically fills gaps in location traces with the help of data from other (co-located) users. Using data from 195 users, collected over a 2-year period, we show that this approach yields fewer and larger segments, where each segment accurately represents the presence of a user at a significant personal place.