B$^2$F-Map: Crowd-sourced Mapping with Bayesian B-spline Fusion

TL;DR

This paper introduces B$^2$F-Map, a Bayesian B-spline fusion approach for scalable, uncertainty-aware crowd-sourced high-definition map generation using monocular cameras, GNSS, and IMU data.

Contribution

It presents a complete pipeline integrating Bayesian B-spline fusion for lane mapping, handling uncertainties and differing density representations in crowd-sourced data.

Findings

Produces geometrically consistent lane-level maps

Handles uncertainties in map fusion effectively

Validated on large-scale real-world datasets

Abstract

Crowd-sourced mapping offers a scalable alternative to creating maps using traditional survey vehicles. Yet, existing methods either rely on prior high-definition (HD) maps or neglect uncertainties in the map fusion. In this work, we present a complete pipeline for HD map generation using production vehicles equipped only with a monocular camera, consumer-grade GNSS, and IMU. Our approach includes on-cloud localization using lightweight standard-definition maps, on-vehicle mapping via an extended object trajectory (EOT) Poisson multi-Bernoulli (PMB) filter with Gibbs sampling, and on-cloud multi-drive optimization and Bayesian map fusion. We represent the lane lines using B-splines, where each B-spline is parameterized by a sequence of Gaussian distributed control points, and propose a novel Bayesian fusion framework for B-spline trajectories with differing density representation, enabling principled handling of uncertainties. We evaluate our proposed approach, B$^2$F-Map, on large-scale real-world datasets collected across diverse driving conditions and demonstrate that our method is able to produce geometrically consistent lane-level maps.