As unmanned aerial vehicles (UAVs) are considered for a wider variety of military and commercial applications, the ability to navigate autonomously in unknown and hazardous environments is increasingly vital to the effectiveness of UAVs. Reliable and efficient obstacle detection is a fundamental prerequisite to performing autonomous navigation in an unknown environment. Traditional two-dimensional (planar) obstacle detection techniques, though computationally friendly, are often insufficient for safe navigation through complex environments in which commanded trajectories are simultaneously restricted vertically and horizontally by multiple buildings or by increases in terrain elevation. To this end, a pan/tilt-mounted laser rangefinder is explored as a means of identifying and characterizing potential obstacles in three dimensions (3D). From GPS position data and inertial sensor measurements, the filtered laser rangefinder data are transformed into local inertial coordinates and compiled into a dynamic three-dimensional grid-based mapping of the specified domain. Utilizing the grid-based map data, path planning algorithms generate the necessary obstacle avoidance trajectories. The Georgia Tech GTMax UAV helicopter and simulation environment provide a suitable test-bed for verification of the proposed obstacle detection and avoidance methodology.