An Analytical Lidar Sensor Model Based on Ray Path Information

TL;DR

This paper presents a new physical lidar sensor model that improves mapping and localization in unstructured environments by utilizing ray path information and out-of-range measurements, outperforming existing models.

Contribution

The authors introduce a consistent physical lidar model that incorporates ray path data and out-of-range measurements, enhancing mapping and localization accuracy.

Findings

Model maximizes data likelihood in mapping.

Outperforms state-of-the-art sensor models in experiments.

Effectively handles unstructured environments.

Abstract

Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a model of the specific sensor. Over the past years, lidar sensors have become a popular choice for mapping and localization. However, many common lidar models perform poorly in unstructured, unpredictable environments, they lack a consistent physical model for both mapping and localization, and they do not exploit all the information the sensor provides, e.g. out-of-range measurements. In this paper, we introduce a consistent physical model that can be applied to mapping as well as to localization. It naturally deals with unstructured environments and makes use of both out-of-range measurements and information about the ray path. The approach can be seen as a generalization of the well-established reflection model, but in addition to counting ray reflections and traversals in a specific map cell, it considers the distances that all rays travel inside this cell. We prove that the resulting map maximizes the data likelihood and demonstrate that our model outperforms state-of-the-art sensor models in extensive real-world experiments.