Lars Hammarstrand
Lars Hammarstrand is an Associate Professor in the Signal processing research group. His main research interests are in the intersection of machine learning and Bayesian inference, especially with application to (visual) localization and mapping, sensor fusion, nonlinear filtering, and object tracking.
Showing 46 publications
Long-Term Visual Localization Revisited
DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
Extended Object Tracking Using Sets of Trajectories with a PHD Filter
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
Using Image Sequences for Long-Term Visual Localization
Radar communications for combating mutual interference of FMCW radars
A cross-season correspondence dataset for robust semantic segmentation
Semantic Match Consistency for Long-Term Visual Localization
Long-term Visual Localization using Semantically Segmented Images
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
Variational Bayesian Expectation Maximization for Radar Map Estimation
Coordination of Cooperative Autonomous Vehicles: Toward safer and more efficient road transportation
Poisson Multi-Bernoulli Radar Mapping Using Gibbs Sampling
Driver-Gaze Zone Estimation Using Bayesian Filtering and Gaussian Processes
Using a single band GNSS receiver to improve relative positioning in autonomous cars
Long-range road geometry estimation using moving vehicles and road-side observations
Flipping a PhD course using movies from a MOOC
Variational Bayesian EM for SLAM
Bayesian Road Estimation Using Onboard Sensors
Vehicle self-localization using off-the-shelf sensors and a detailed map
Road Geometry Estimation Using a Precise Clothoid Road Model and Observations of Moving Vehicles
A CPHD Filter for Tracking With Spawning Models
A Probabilistic Framework for Decision-Making in Collision Avoidance Systems
A CPHD filter for tracking with spawning models - including a FISST based derivation
Multitarget Sensor Resolution Model and Joint Probabilistic Data Association
A Study of MAP Estimation Techniques for Nonlinear Filtering
Adaptive Radar Sensor Model for Tracking Structured Extended Objects
A cardinality preserving multitarget multi-Bernoulli RFS tracker
Extended Object Tracking using a Radar Resolution Model
A New Vehicle Motion Model for Improved Predictions and Situation Assessment
Multitarget sensor resolution model for arbitrary target numbers
Tracking and radar sensor modelling for automotive safety systems
Joint probabilistic data association filter for partially unresolved target groups
Random Set Based Road mapping using Radar Measurements
Multi-Target Tracking with Partially Unresolved Measurements
Road intensity based mapping using radar measurements with a probability hypothesis density filter
SEFS–Results on Sensor Data Fusion System Development
A Design Architecture For Sensor Data Fusion Systems With Application To Automotive Safety
HMI principles for lateral safe applications
INSAFES HCI principles for integrated ADAS applications
Tracking vehicles using radar detections
Joint driver intention classification and tracking of vehicles
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Showing 6 research projects
Deep MultiModal Learning for Automotive Applications
Guaranteed Bounds for Vehicle Motion State Estimates for automated driving
Robust and precise Semi-Supervised Learning schemes
COPPLAR CampusShuttle cooperative perception & planning platform
Fordonspositionering och ruttprediktion
SICS - Safe Interaction, Connectivity and State v2