6 papers
BF-Map: Crowd-sourced Mapping with Bayesian B-spline Fusion
Yiping Xie, Yuxuan Xia, Erik Stenborg +5
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 negl…
Bayesian Simultaneous Localization and Multi-Lane Tracking Using Onboard Sensors and a SD Map
Yuxuan Xia, Erik Stenborg, Junsheng Fu +1
High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present…
Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It
Adam Lilja, Junsheng Fu, Erik Stenborg +1
The task of online mapping is to predict a local map using current sensor observations, e.g. from lidar and camera, without relying on a pre-built map. State-of-the-art methods are…
Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
Måns Larsson, Erik Stenborg, Carl Toft +3
Long-term visual localization is the problem of estimating the camera pose of a given query image in a scene whose appearance changes over time. It is an important problem in pract…
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
Måns Larsson, Erik Stenborg, Lars Hammarstrand +3
In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmen…
Long-term Visual Localization using Semantically Segmented Images
Erik Stenborg, Carl Toft, Lars Hammarstrand
Robust cross-seasonal localization is one of the major challenges in long-term visual navigation of autonomous vehicles. In this paper, we exploit recent advances in semantic segme…