This technology is an ego-motion estimation method for Simultaneous Localization and Mapping (SLAM) that maps FMCW radar target detection data into a 2D angle-velocity domain. It calculates a robot's movement speed and rotation angle without external sensors by analyzing data correlations between consecutive scans and applying linear regression to trend lines.
Existing SLAM systems require additional hardware such as motor encoders or gyro sensors to estimate ego-motion, which increases system complexity and cost. Furthermore, laser and camera-based sensors often suffer from performance degradation in low-light or adverse weather conditions.
This technology converts the relative velocity and angle information of targets acquired from radar sensors into a binary matrix. It then derives the rotation angle through forward and backward cross-correlation operations and calculates movement speed from the velocity-axis intercept by performing linear regression on the detection point trend line in the angle-velocity domain. This enables robust localization in adverse conditions without the need for additional sensors. Because it functions even in environments where cameras and LiDAR are ineffective—such as smoke, dust, or low-light conditions—it significantly enhances the reliability of disaster search robots and industrial autonomous equipment.
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