Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance
This work addresses place recognition for autonomous vehicles using radar, offering incremental improvements to existing sequence-based methods by adapting them for radar's rotational invariance.
The paper tackles the problem of place recognition using radar scans in autonomous driving by integrating a rotationally-invariant metric embedding into sequence-based trajectory matching, achieving a 30% boost in recall at high precision over a nearest neighbor approach on 26 km of urban driving data.
This paper details an application which yields significant improvements to the adeptness of place recognition with Frequency-Modulated Continuous-Wave radar - a commercially promising sensor poised for exploitation in mobile autonomy. We show how a rotationally-invariant metric embedding for radar scans can be integrated into sequence-based trajectory matching systems typically applied to videos taken by visual sensors. Due to the complete horizontal field of view inherent to the radar scan formation process, we show how this off-the-shelf sequence-based trajectory matching system can be manipulated to detect place matches when the vehicle is travelling down a previously visited stretch of road in the opposite direction. We demonstrate the efficacy of the approach on 26 km of challenging urban driving taken from the largest radar-focused urban autonomy dataset released to date -- showing a boost of 30% in recall at high levels of precision over a nearest neighbour approach.