Uploaded June 2021 | Updated September 2026, 3 weeks ago
This paper presents fast and informative local world representations for the safe navigation of legged robots during Visual Teach and Repeat (VT&R) missions. We show how meaningful representations can be computed from the local elevation map using simple image processing techniques, and its closed loop integration with a Riemannian Motion Policies-based twist controller. We demonstrate our approach with ANYmal C in a challenging indoor environment.
Accepted to the 5th Full-Day Workshop on Legged Robots at ICRA 2021
Authors: Matías Mattamala, Nived Chebrolu, Maurice Fallon
PDF: drive.google.com/file/d/1pYGr4q2F-uGriIovyVI859n7xHnoSCsc/view?usp=sharing
Presentation: youtu.be/Dvy6L799dn4
This paper presents fast and informative local world representations for the safe navigation of legged robots during Visual Teach and Repeat (VT&R) missions. We show how meaningful representations can be computed from the local elevation map using simple image processing techniques, and its closed loop integration with a Riemannian Motion Policies-based twist controller. We demonstrate our approach with ANYmal C in a challenging indoor environment.
Accepted to the 5th Full-Day Workshop on Legged Robots at ICRA 2021
Authors: Matías Mattamala, Nived Chebrolu, Maurice Fallon
PDF: drive.google.com/file/d/1pYGr4q2F-uGriIovyVI859n7xHnoSCsc/view?usp=sharing
Presentation: youtu.be/Dvy6L799dn4
![[Presentation] Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks](https://i.ytimg.com/vi/mNMdlNkWeVM/mqdefault.jpg "[Presentation] Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks
Video presentation of the article https://arxiv.org/abs/2010.09232
Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks
Yiduo Wang, Nils Funk, Milad Ramezani, Sotiris Papatheodorou, Marija Popovic, Marco Camurri, Stefan Leutenegger, Maurice Fallon
IEEE International Conference on Robotics and Automation (ICRA), 2021
Abstract:
We present an efficient, elastic 3D LiDAR reconstruction framework which can reconstruct up to maximum LiDAR ranges (60 m) at multiple frames per second, thus enabling robot exploration in large-scale environments. Our approach only requires a CPU. We focus on three main challenges of large-scale reconstruction: integration of long-range LiDAR scans at high frequency, the capacity to deform the reconstruction after loop closures are detected, and scalability for long-duration exploration. Our system extends upon a state-of-the-art efficient RGB-D volumetric reconstruction technique, called supereight, to support LiDAR scans and a newly developed submapping technique to allow for dynamic correction of the 3D reconstruction. We then introduce a novel pose graph clustering and submap fusion feature to make the proposed system more scalable for large environments. We evaluate the performance using two public datasets including outdoor exploration with a handheld device and a drone, and with a mobile robot exploring an underground room network. Experimental results demonstrate that our system can reconstruct at 3 Hz with 60 m sensor range and ~5 cm resolution, while state-of-the-art approaches can only reconstruct to 25 cm resolution or 20 m range at the same frequency.")
