In this work, we introduce RadarTrack, an innovative ego-speed estimation framework utilizing a single-chip millimeter-wave (mmWave) radar to deliver robust speed estimation for mobile platforms. Unlike previous methods that depend on cross-modal learning and computationally intensive Deep Neural Networks (DNNs), RadarTrack utilizes a novel phase-based speed estimation approach. This method effectively overcomes the limitations of conventional ego-speed estimation approaches which rely on doppler measurements and static surroundings. RadarTrack is designed for low-latency operation on embedded platforms, making it suitable for real-time applications where speed and efficiency are critical. Our key contributions include the introduction of a novel phase-based speed estimation technique solely based on signal processing and the implementation of a real-time prototype validated through extensive real-world evaluations. By providing a reliable and lightweight solution for ego-speed estimation, RadarTrack holds significant potential for a wide range of applications, including micro-robotics, augmented reality, and autonomous navigation.

In this demonstration we introduce a novel approach to ego-speed estimation using a single-chip Commercial-Off-the-Shelf (COTS) millimeter-wave (mmWave) radar. Contrary to previous approaches that are cross-modal learning based and dependent on the computationally expensive nature of DNN's, our proposed approach RadarTrack is based on a phase-based approach to speed estimation. Our approach successfully overcomes the drawbacks of traditional ego-speed estimation methods that are doppler-based and static environment dependent. RadarTrack is intended to support low-latency execution on embedded systems such that it can be used in real-time applications where efficiency and speed are equally important. We have also created a real-time visualizer which is capable of recording the phasebased ego-speed together with state-of-the-art doppler-based speed estimation and comparatively demonstrate how phasebased ego-speed estimation can better record the speed of an ego-vehicle.

Precise ego-motion measurement is crucial for various applications, including robotics, augmented reality, and autonomous navigation. In this poster, we propose mmPhase, an odometry framework based on single-chip millimetre-wave (mmWave) radar for robust ego-motion estimation in mobile platforms without requiring additional modalities like the visual, wheel, or inertial odometry. mmPhase leverages a phase-based velocity estimation approach to overcome the limitations of conventional doppler resolution. For real-world evaluations of mmPhase we have developed an ego-vehicle prototype. Compared to the state-of-the-art baselines, mmPhase shows superior performance in ego-velocity estimation.