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Our long-term goal is to develop a general solution to the lifelong robotic object discovery (LROD) problem: to discover new objects in the environment while the robot operates, for as long as the robot operates. In this paper, we consider the first step towards LROD: we automatically process the raw data stream of an entire workday of a robotic agent to discover objects. Our key contribution to achieve this goal is to incorporate domain knowledge (robotic metadata) in the discovery process, in addition to visual data. We propose a general graph-based formulation for LROD in which generic domain knowledge is encoded as constraints. To make long-term object discovery feasible, we encode into our formulation the natural constraints and non-visual sensory information in service robotics. A key advantage of our generic formulation is that we can add, modify, or remove sources of domain knowledge dynamically, as they become available or as conditions change. In our experiments, we show that by adding domain knowledge we discover 2.7× more objects and decrease processing time 190 times. With our optimized implementation, HerbDisc, we show for the first time a system that processes a video stream of 6 h 20 min of continuous exploration in cluttered human environments (and over half a million images) in 18 min 34 s, to discover 206 new objects with their 3D models. [ABSTRACT FROM PUBLISHER]