Lars Hammarstrand
Lars Hammarstrand is an Assistant 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 mapping, self- localization, object tracking, sensor data fusion for self-driving vehicles. Lars is the coordinator for Chalmers efforts surrounding the development of a self-driving vehicle through the project COPPLAR: He also supervises PhD students and teaches graduate and undergraduate courses in, e.g., non-linear filtering and linear systems and transforms.
Showing 43 publications
Long-Term Visual Localization Revisited
Using Image Sequences for Long-Term Visual Localization
A cross-season correspondence dataset for robust semantic segmentation
Radar communications for combating mutual interference of FMCW radars
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Long-term Visual Localization using Semantically Segmented Images
Semantic Match Consistency for Long-Term Visual Localization
Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
Coordination of Cooperative Autonomous Vehicles: Toward safer and more efficient road transportation
Using a single band GNSS receiver to improve relative positioning in autonomous cars
Driver-Gaze Zone Estimation Using Bayesian Filtering and Gaussian Processes
Long-range road geometry estimation using moving vehicles and road-side observations
Variational Bayesian Expectation Maximization for Radar Map Estimation
Poisson Multi-Bernoulli Radar Mapping Using Gibbs Sampling
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 - including a FISST based derivation
A Probabilistic Framework for Decision-Making in Collision Avoidance Systems
A CPHD Filter for Tracking With Spawning Models
Multitarget Sensor Resolution Model and Joint Probabilistic Data Association
Adaptive Radar Sensor Model for Tracking Structured Extended Objects
Extended Object Tracking using a Radar Resolution Model
A cardinality preserving multitarget multi-Bernoulli RFS tracker
A Study of MAP Estimation Techniques for Nonlinear Filtering
A New Vehicle Motion Model for Improved Predictions and Situation Assessment
Tracking and radar sensor modelling for automotive safety systems
Road intensity based mapping using radar measurements with a probability hypothesis density filter
Multitarget sensor resolution model for arbitrary target numbers
Random Set Based Road mapping using Radar Measurements
Multi-Target Tracking with Partially Unresolved Measurements
Joint probabilistic data association filter for partially unresolved target groups
SEFS–Results on Sensor Data Fusion System Development
A Design Architecture For Sensor Data Fusion Systems With Application To Automotive Safety
Tracking vehicles using radar detections
HMI principles for lateral safe applications
INSAFES HCI principles for integrated ADAS applications
Joint driver intention classification and tracking of vehicles
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Showing 3 research projects
COPPLAR CampusShuttle cooperative perception & planning platform
Fordonspositionering och ruttprediktion
SICS - Safe Interaction, Connectivity and State v2