Student Projects
Learning LiDAR Registration Correspondences
The goal of this project is to train a neural network to learn to predict geometrically consistent point correspondences for accurate LiDAR odometry.
Keywords
Pointcloud, Lidar, Registration, Odometry, Neural Network, Deep Learning, Machine Learning, Robotics
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Master Thesis
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Published since: 2025-03-24 , Earliest start: 2025-04-01 , Latest end: 2026-02-01
Organization Autonomous Systems Lab
Hosts Tuna Turcan , Pfreundschuh Patrick
Topics Information, Computing and Communication Sciences , Engineering and Technology
Multi-Sensor Semantic Odometry
Semantic segmentation augments visual information from cameras or geometric information from LiDARs by classifying what objects are present in a scene. Fusing this semantic information with visual or geometric sensor data can improve the odometry estimate of a robot moving through the scene. Uni-modal semantic odometry approaches using camera images or LiDAR point clouds have been shown to outperform traditional single-sensor approaches. However, multi-sensor odometry approaches typically provide more robust estimation in degenerate environments.
Keywords
Odometry, Sensor fusion, Semantics
Labels
Semester Project , Master Thesis
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Published since: 2025-02-17 , Earliest start: 2024-07-14 , Latest end: 2025-01-31
Applications limited to ETH Zurich
Organization Autonomous Systems Lab
Hosts Chli Margarita , Mascaro Rubén
Topics Information, Computing and Communication Sciences
LiDAR-Visual-Inertial Odometry with a Unified Representation
Lidar-Visual-Inertial odometry approaches [1-3] aim to overcome the limitations of the individual sensing modalities by estimating a pose from heterogenous measurements. Lidar-inertial odometry often diverges in environments with degenerate geometric structures and visual-inertial odometry can diverge in environments with uniform texture. Many existing lidar-visual-inertial odometry approaches use independent lidar-inertial and visual-inertial pipelines [2-3] to compute odometry estimates that are combined in a joint optimisation to obtain a single pose estimate. These approaches are able to obtain a robust pose estimate in degenerate environments but often underperform lidar-inertial or visual-inertial methods in non-degenerate scenarios due to the complexity of maintaining and combining odometry estimates from multiple representations.
Keywords
Odometry, SLAM, Sensor Fusion
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Semester Project , Master Thesis
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Published since: 2025-02-17 , Earliest start: 2025-01-05 , Latest end: 2025-06-30
Applications limited to ETH Zurich
Organization Autonomous Systems Lab
Hosts Mascaro Rubén , Chli Margarita
Topics Information, Computing and Communication Sciences
Odometry and Mapping in Dynamic Environments
Existing lidar-inertial odometry approaches (e.g., FAST-LIO2 [1]) are capable of providing sufficiently accurate pose estimation in structured environments to capture high quality 3D maps of static structures in real-time. However, the presence of dynamic objects in an environment can reduce the accuracy of the odometry estimate and produce noisy artifacts in the captured 3D map. Existing approaches to handling dynamic objects [2-4] focus on detecting and filtering them from the captured 3D map but typically operate independently from the odometry pipeline, which means that the dynamic filtering does not improve the pose estimation accuracy.
Keywords
Odometry, Mapping, SLAM, Dynamic Environments
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Semester Project , Master Thesis
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Published since: 2025-02-17 , Earliest start: 2025-01-05 , Latest end: 2025-06-30
Applications limited to ETH Zurich
Organization Autonomous Systems Lab
Hosts Mascaro Rubén , Chli Margarita
Topics Information, Computing and Communication Sciences , Engineering and Technology
Generating Detailed 3D Objects from Rough 3D Primitives
This project focuses on the generation of detailed 3D models from a user-specified set of 3D cuboids.
Keywords
Generative 3D Modelling, Diffusion Models, 3D Vision
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2025-11-30
Organization Autonomous Systems Lab
Hosts Claessens Liesbeth
Topics Information, Computing and Communication Sciences
Note on plagiarism
We would like to suggest every student, irrespective of the type of project (Bachelor, Semester, Master, ...), to make himself/herself familiar with ETH rules regarding plagiarism