Smart interaction between autonomous centimeter-scale unmanned aerial vehicles (i.e., nano-UAVs) and Internet of Things (IoT) sensor nodes is an upcoming high-impact scenario. This work tackles precise 3-D localization of indoor edge nodes with an autonomous nano-UAV without prior knowledge of their position. We employ ultrawideband (UWB) and wake-up radio (WUR) technologies: we perform UWB-based ranging and data exchange between the nano-UAV and the nodes, while the WUR minimizes the sensors’ power consumption. UWB-based precise localization requires addressing multiple sources of error, such as UWB-ranging noise and UWB antennas’ uneven radiation pattern. The limited computational resources aboard a nano-UAV further complicate this scenario, requiring real-time execution of the localization algorithm within a microcontroller unit (MCU). We propose a novel UWB-based localization system for nano-UAVs, composed by: 1) a lightweight localization algorithm; 2) an optimal flight strategy; and 3) a ranging-error-correction model. Our 3-D flight policy requires only five UWB measurements to feed the localization algorithm, which bounds the localization error within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {28 \, \text {c} \text {m} }$ </tex-math></inline-formula> and runs in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {1.2 \text {m} \text {s} }$ </tex-math></inline-formula> on a Cortex-M4 MCU. Localization accuracy is improved by an additional 25% thanks to a novel error-correction model. Leveraging the WUR, the entire localization/data-exchange cycle costs only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {24 \, \text {m} \text {J} }$ </tex-math></inline-formula> at the sensor node, which is 50 times more energy efficient than the state of the art with comparable localization accuracy.