DICP: Doppler Iterative Closest Point Algorithm

TL;DR

This paper introduces Doppler ICP, a novel point cloud registration algorithm that leverages Doppler velocity measurements to improve accuracy and convergence in feature-sparse environments.

Contribution

The paper proposes a new Doppler velocity objective function and joint optimization approach to enhance point cloud registration in challenging scenarios.

Findings

Significant improvement in registration accuracy.

Faster convergence compared to classical ICP.

Effective in feature-denied environments.

Abstract

In this paper, we present a novel algorithm for point cloud registration for range sensors capable of measuring per-return instantaneous radial velocity: Doppler ICP. Existing variants of ICP that solely rely on geometry or other features generally fail to estimate the motion of the sensor correctly in scenarios that have non-distinctive features and/or repetitive geometric structures such as hallways, tunnels, highways, and bridges. We propose a new Doppler velocity objective function that exploits the compatibility of each point's Doppler measurement and the sensor's current motion estimate. We jointly optimize the Doppler velocity objective function and the geometric objective function which sufficiently constrains the point cloud alignment problem even in feature-denied environments. Furthermore, the correspondence matches used for the alignment are improved by pruning away the points from dynamic targets which generally degrade the ICP solution. We evaluate our method on data collected from real sensors and from simulation. Our results show that with the added Doppler velocity residual terms, our method achieves a significant improvement in registration accuracy along with faster convergence, on average, when compared to classical point-to-plane ICP that solely relies on geometric residuals.