Reliable positioning and mapping in dense urban environments remain challenging due to signal blockage, multipath, and dynamic scenes. Progress on multi-sensor integrated positioning and visual/lidar SLAM has been driven by open datasets, yet most existing resources are either perception-centric with limited raw navigation data, focused on controlled environments, or built around outdated software platforms and/or data formats. In this paper, we present the Melbourne Multi-Sensor Urban Positioning and Mapping Dataset, a new resource targeting urban vehicle navigation and mapping tasks. The dataset was collected using a custom mobile mapping platform equipped with a tactical-grade INS, a survey-grade Leica GNSS receiver, a low-cost UBLOX GNSS receiver, a high-resolution Ouster OS1 128 lidar, and four industrial FLIR cameras providing 360° coverage. Seven data collection trips were recorded on dynamic streets in several inner suburbs of Melbourne, including multiple closed loops and a repeated route with day–night variation. For better compatibility and future-proofing, all raw data are provided as standard ROS2 message streams in MCAP format, complemented by commonly used individual formats and GNSS products for multi-sensor integrations. We benchmark three GNSS–based positioning packages (RTKLib, Net Diff and Ginan) and four state-of-the-art lidar(-inertial) odometry/SLAM methods (FAST-LIO2, KISS-ICP, KISS-SLAM and PIN-SLAM), demonstrating the applicability and compatibility of our dataset for modern positioning and mapping software pipelines. The dataset is designed as a robust, ROS2-native testbed for research on GNSS/IMU/lidar/camera fusion for the testing and validation of vehicle positioning and mapping in urban environments, which is available open-source at https://github.com/zjjdes/melbourne_dataset.