Lennart Svensson

Professor

Chalmers University of Technology

Sweden

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Professor Lennart Svensson is a faculty member at Chalmers University of Technology in Sweden. His research areas encompass advanced topics in robotics and computer vision, including multimodal motion prediction, trajectory analysis, and simultaneous localization and mapping (SLAM). He has authored several recent articles on dynamic obstacle avoidance, belief propagation methods, and deep learning techniques for multi-object tracking.

Articles (16)

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2021

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2021

Collaborators (8)

Mikko Valkama

University of Tampere

FINLAND

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Hyowon Kim

Chungbuk National University

SOUTH KOREA

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Raivo Sell

ESTONIA

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Henk Wymeersch

Professor at Chalmers University of Technology

Chalmers University of Technology

SWEDEN

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Mauro Bellone

ESTONIA

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Jukka Talvitie

University of Tampere

FINLAND

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Angel F. Garcia-Fernandez

University of Liverpool

UNITED KINGDOM

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Florian Meyer

University of California, San Diego

UNITED STATES

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Lennart Svensson

Research Interests

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About

Articles (16)

Batch SLAM With PMBM Data Association Sampling and Graph-Based Optimization

Poisson Multi-Bernoulli Mixtures for Sets of Trajectories

Future-Oriented Navigation: Dynamic Obstacle Avoidance With One-Shot Energy-Based Multimodal Motion Prediction

Graph GOSPA Metric: A Metric to Measure the Discrepancy Between Graphs of Different Sizes

Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM

Markov Chain Monte Carlo Multiscan Data Association for Sets of Trajectories

Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation

Deep Learning for Model-Based Multi-Object Tracking

Poisson Multi-Bernoulli Mixture Filter With General Target-Generated Measurements and Arbitrary Clutter

PMBM-Based SLAM Filters in 5G mmWave Vehicular Networks

A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM

Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering

Multiple Object Trajectory Estimation Using Backward Simulation

Tracking Multiple Spawning Targets Using Poisson Multi-Bernoulli Mixtures on Sets of Tree Trajectories

Lidar–Camera Semi-Supervised Learning for Semantic Segmentation

Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar

Collaborators (8)

Mikko Valkama

Hyowon Kim

Raivo Sell

Henk Wymeersch

Mauro Bellone

Jukka Talvitie

Angel F. Garcia-Fernandez

Florian Meyer

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