A visual study of ICP variants for Lidar Odometry

TL;DR

This paper visually analyzes various ICP algorithm variants used in lidar odometry, highlighting their performance issues under real-world conditions and proposing new filtering methods to improve accuracy.

Contribution

It introduces a visualization technique for ICP objective functions and proposes novel filtering methods to handle dynamic objects and ego blind spots in lidar odometry.

Findings

Visualization reveals performance differences among ICP variants.

Filtering methods improve odometry accuracy in dynamic environments.

Proposed solutions address ego blind spot issues.

Abstract

Odometry with lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Many implementations of lidar odometry use variants of the Iterative Closest Point (ICP) algorithm. Real-world effects such as dynamic objects, non-overlapping areas, and sensor noise diminish the accuracy of ICP. We build on a recently proposed method that makes these effects visible by visualizing the multidimensional objective function of ICP in two dimensions. We use this method to study different ICP variants in the context of lidar odometry. In addition, we propose a novel method to filter out dynamic objects and to address the ego blind spot problem.