An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking
This addresses the challenge of improving radar-based perception for autonomous vehicles, though it appears incremental as it builds on existing RLS techniques for a specific domain.
The paper tackles the problem of estimating instantaneous velocity from noisy radar data for dynamic objects, presenting a Recursive Least Squares (RLS) based method that runs in real-time with frame execution times under 30 ms in dense traffic scenarios.
Radar sensors have become an important part of the perception sensor suite due to their long range and their ability to work in adverse weather conditions. However, several shortcomings such as large amounts of noise and extreme sparsity of the point cloud result in them not being used to their full potential. In this paper, we present a novel Recursive Least Squares (RLS) based approach to estimate the instantaneous velocity of dynamic objects in real-time that is capable of handling large amounts of noise in the input data stream. We also present an end-to-end pipeline to track extended objects in real-time that uses the computed velocity estimates for data association and track initialisation. The approaches are evaluated using several real-world inspired driving scenarios that test the limits of these algorithms. It is also experimentally proven that our approaches run in real-time with frame execution time not exceeding 30 ms even in dense traffic scenarios, thus allowing for their direct implementation on autonomous vehicles.