Localization with Anticipation for Autonomous Urban Driving in Rain

TL;DR

This paper introduces a novel localization algorithm for autonomous urban vehicles in rainy conditions, leveraging visual data, a global reference path, and vehicle motion models to improve pose estimation accuracy.

Contribution

The proposed method uniquely combines anticipation based on road context and visual data to enhance localization under adverse weather conditions.

Findings

50.83% improvement in localization accuracy in rain

34.32% improvement in clear weather

Validated on Oxford Robotcar and Singapore datasets

Abstract

This paper presents a localization algorithm for autonomous urban vehicles under rain weather conditions. In adverse weather, human drivers anticipate the location of the ego-vehicle based on the control inputs they provide and surrounding road contextual information. Similarly, in our approach for localization in rain weather, we use visual data, along with a global reference path and vehicle motion model for anticipating and better estimating the pose of the ego-vehicle in each frame. The global reference path contains useful road contextual information such as the angle of turn which can be potentially used to improve the localization accuracy especially when sensors are compromised. We experimented on the Oxford Robotcar Dataset and our internal dataset from Singapore to validate our localization algorithm in both clear and rain weather conditions. Our method improves localization accuracy by 50.83% in rain weather and 34.32% in clear weather when compared to baseline algorithms.