As robots are developed for large-scale applications in autonomous driving, package delivery, and agriculture, there is a growing need for affordable and reliable depth sensing. Robots use active illumination sensors like scanning LIDAR and depth cameras to perceive their worlds. Scanning LIDAR is prevalent because it offers long-range, robust sensing, but it is expensive and only captures sparse pointmeasurements. Consumer depth cameras, on the other hand, are inexpensive and produce high-rate, dense depth measurements but fail outdoors in bright light. This thesis developed active illumination depth cameras and sensing methodologies that combine the robustness of scanning LIDAR with the speed, sampling density, and economy of consumer depth cameras. Rather than sample the entirescene at once like consumer depth cameras or with points like LIDAR, the key approach uses sheets of projected light and imaging to rapidly sample the scene alonga single line at a time. Using this approach, four contributions have been made. The first is a contributionto the development of a light sheet depth imaging device that applies the concept of epipolar imaging to continuous-wave time-of-flight cameras. The resulting depth camera can see up to 15 meters in bright sunlight and is robust to global illumination and motion. The next contribution developed a second generation of this camera that demonstrated sensing ranges up to 50 meters. The third contribution uses the projected sheets of light and imaging to triangulate and sense along a 3D line. By sweeping this line through the volume with galvomirrors, a programmable light curtainis formed that detects objects along its surface at five frames per second. Finally, rapid imaging of programmable light curtains at 60 frames per second was enabledwith the custom development of a device that uses the rolling shutter of a camera to steer the imaging plane instead of a galvomirror. The speed and selectivity providedby this device enabled applications in agile depth sensing where scenes are adaptively sampled based on detected regions of interest. The research developed in this thesis contributes methods and hardware for high resolutiondepth imaging that works in challenging conditions, provides methods for computationally inexpensive agile depth sensing, and has the economics that could enable next generation and wide-scale applications in mobile robotics, agriculture, and industrial manufacturing.