Indoor self-localization has become a highly desirable system function for smartphones. The existing systems based on imaging, radio frequency, and geomagnetic sensing may have sub-optimal performance when their limiting factors prevail. In this paper, we present a new indoor simultaneous localization and mapping (SLAM) system that is based on the smartphone&#x0027;s built-in audio hardware and inertial measurement unit (IMU). Our system uses a smartphone&#x0027;s loudspeaker to emit near-inaudible chirps and then the microphone to record the acoustic echoes from the indoor environment. The echoes contain the smartphone&#x0027;s location information with sub-meter granularity. To enable SLAM, we apply contrastive learning to train an echoic location feature (ELF) extractor, such that the loop closures on the smartphone&#x0027;s trajectory can be accurately detected from the associated ELF trace. The detection results effectively regulate the IMU-based trajectory reconstruction. The reconstructed trajectories are used for <italic>trajectory map superimposition</italic> and <italic>room geometry reconstruction</italic>. Extensive experiments show that our SLAM achieves median localization errors of <inline-formula><tex-math notation="LaTeX">$\text{0.1}\,\text{m}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{0.53}\,\text{m}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\text{0.4}\,\text{m}$</tex-math></inline-formula> in a living room, an office, and a shopping mall, and outperforms both the Wi-Fi and geomagnetic SLAM systems. The room geometry reconstruction achieves up to 4&#x00D7; lower errors compared with the latest echo-based approaches.