This work was partially supported by the Penn State Applied Research Laboratory. Date of publication Februdate of current version August 27, 2020. Manuscript received revised Septemand Decemaccepted January 30, 2020. T2 - Ego-Motion Estimation from Doppler and Spatial Data in RADAR Images The real-world results demonstrate accurate ego-motion estimates that are particularly suited for integration within a 'tightly-coupled' sensor fusion framework.", Unlike other methods, RADARODO estimates ego-motion along with its predicted uncertainty from a single RADAR sensor without depending on a lever-arm offset or the zero side-slip assumption. Secondly, it directly analyzes RADAR images to estimate rotational motion via correspondence-free non-linear optimization. First, it decouples sensor translational and rotational motion by estimating them from Doppler and spatial data, respectively. RADARODO is differentiated from similar techniques by two novel attributes. This paper presents the novel real-time RADAR-based ego-motion estimation algorithm, RADARODO (RADAR Odometry). RADAR measurements provide unique capabilities for ego-motion estimation while also posing unique challenges. The real-world results demonstrate accurate ego-motion estimates that are particularly suited for integration within a 'tightly-coupled' sensor fusion framework.Ībstract = "Accurate and robust ego-motion estimation is critical for aiding autonomous vehicle localization in environments devoid of map landmarks or accurate GNSS measurements. Accurate and robust ego-motion estimation is critical for aiding autonomous vehicle localization in environments devoid of map landmarks or accurate GNSS measurements.
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