Hypermaps | Closing the complexity gap in robotic mapping

conference articles

1. IROS

   

   Bayesian Floor Field: Transferring people flow predictions across environments

   Francesco Verdoja, Tomasz Piotr Kucner, and Ville Kyrki

   In 2024 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Oct 2024

   accepted

   Abs arXiv Bib PDF Code

   Mapping people dynamics is a crucial skill for robots, because it enables them to coexist in human-inhabited environments. However, learning a model of people dynamics is a time consuming process which requires observation of large amount of people moving in an environment. Moreover, approaches for mapping dynamics are unable to transfer the learned models across environments: each model is only able to describe the dynamics of the environment it has been built in. However, the impact of architectural geometry on people’s movement can be used to anticipate their patterns of dynamics, and recent work has looked into learning maps of dynamics from occupancy. So far however, approaches based on trajectories and those based on geometry have not been combined. In this work we propose a novel Bayesian approach to learn people dynamics able to combine knowledge about the environment geometry with observations from human trajectories. An occupancy-based deep prior is used to build an initial transition model without requiring any observations of pedestrian; the model is then updated when observations become available using Bayesian inference. We demonstrate the ability of our model to increase data efficiency and to generalize across real large-scale environments, which is unprecedented for maps of dynamics.

   @inproceedings{202410_verdoja_bayesian,
     title = {Bayesian Floor Field: Transferring people flow predictions across
                      environments},
     url = {https://arxiv.org/abs/2208.10851},
     booktitle = {2024 {IEEE}/{RSJ} {Int.}\ {Conf.}\ on {Intelligent} {Robots} and 
                      {Systems} ({IROS})},
     author = {Verdoja, Francesco and Kucner, Tomasz Piotr and Kyrki, Ville},
     month = oct,
     year = {2024},
     note = {accepted},
   }

workshop articles

1. ICRA

   

   Evaluating the quality of robotic visual-language maps

   Matti Pekkanen, Tsvetomila Mihaylova, Francesco Verdoja, and Ville Kyrki

   May 2024

   Presented at the “Vision-Language Models for Navigation and Manipulation (VLMNM)” workshop at the IEEE Int. Conf. on Robotics and Automation (ICRA)

   Abs Bib PDF

   Visual-language models (VLMs) have recently been introduced in robotic mapping by using the latent representations, i.e., embeddings, of the VLMs to represent the natural language semantics in the map. The main benefit is moving beyond a small set of human-created labels toward open-vocabulary scene understanding. While there is anecdotal evidence that maps built this way support downstream tasks, such as navigation, rigorous analysis of the quality of the maps using these embeddings is lacking. In this paper, we propose a way to analyze the quality of maps created using VLMs by evaluating two critical properties: queryability and consistency. We demonstrate the proposed method by evaluating the maps created by two state-of-the-art methods, VLMaps and OpenScene, using two encoders, LSeg and OpenSeg, using real-world data from the Matterport3D data set. We find that OpenScene outperforms VLMaps with both encoders, and LSeg outperforms OpenSeg with both methods.

   @online{202405_pekkanen_evaluating,
     title = {Evaluating the quality of robotic visual-language maps},
     url = {https://vlmnm-workshop.github.io},
     author = {Pekkanen, Matti and Mihaylova, Tsvetomila and Verdoja, Francesco and Kyrki, Ville},
     month = may,
     year = {2024},
     note = {Presented at the ``Vision-Language Models for Navigation and 
                      Manipulation (VLMNM)'' workshop at the IEEE Int.\ Conf.\ on 
                      Robotics and Automation (ICRA)},
   }

2. ICRA

   

   Using occupancy priors to generalize people flow predictions

   Francesco Verdoja, Tomasz Piotr Kucner, and Ville Kyrki

   May 2024

   Presented at the “Long-term Human Motion Prediction” workshop at the IEEE Int. Conf. on Robotics and Automation (ICRA)

   Abs Bib PDF

   Mapping people dynamics is a crucial skill for robots, because it enables them to coexist in human-inhabited environments. However, learning a model of people dynamics is a time consuming process which requires observation of large amount of people moving in an environment. Moreover, approaches for mapping dynamics are unable to transfer the learned models across environments: each model is only able to describe the dynamics of the environment it has been built in. However, the impact of architectural geometry on people’s movement can be used to anticipate their patterns of dynamics, and recent work has looked into learning maps of dynamics from occupancy. So far however, approaches based on trajectories and those based on geometry have not been combined. In this work we propose a novel Bayesian approach to learn people dynamics able to combine knowledge about the environment geometry with observations from human trajectories. An occupancy-based deep prior is used to build an initial transition model without requiring any observations of pedestrian; the model is then updated when observations become available using Bayesian inference. We demonstrate the ability of our model to increase data efficiency and to generalize across real large-scale environments, which is unprecedented for maps of dynamics.

   @online{202405_verdoja_using,
     title = {Using occupancy priors to generalize people flow predictions},
     url = {https://motionpredictionicra2024.github.io},
     author = {Verdoja, Francesco and Kucner, Tomasz Piotr and Kyrki, Ville},
     month = may,
     year = {2024},
     note = {Presented at the ``Long-term Human Motion Prediction'' workshop at 
                      the IEEE Int.\ Conf.\ on Robotics and Automation (ICRA)},
   }