Uploaded March 2024 | Updated September 2026, 3 weeks ago
Accepted to ICRA 2024
Website: ori-drs.github.io/projects/silvr
Pre-print: arxiv.org/abs/2403.06877
[Abstract]
We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures. This system adapts the state-of-the-art neural radiance field (NeRF) representation to also incorporate lidar data which adds strong geometric constraints on the depth and surface normals. We exploit the trajectory from a real-time lidar SLAM system to bootstrap a Structure-from-Motion (SfM) procedure to both significantly reduce the computation time and to provide metric scale which is crucial for lidar depth loss. We use submapping to scale the system to large-scale environments captured over long trajectories. We demonstrate the reconstruction system with data from a multi-camera, lidar sensor suite onboard a legged robot, hand-held while scanning building scenes for 600 metres, and onboard an aerial robot surveying a multi-storey mock disaster site-building.
Authors: Yifu Tao , Yash Bhalgat, Lanke Frank Tarimo Fu, Matias Mattamala , Nived Chebrolu, Maurice Fallon
Accepted to ICRA 2024
Website: ori-drs.github.io/projects/silvr
Pre-print: arxiv.org/abs/2403.06877
[Abstract]
We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures. This system adapts the state-of-the-art neural radiance field (NeRF) representation to also incorporate lidar data which adds strong geometric constraints on the depth and surface normals. We exploit the trajectory from a real-time lidar SLAM system to bootstrap a Structure-from-Motion (SfM) procedure to both significantly reduce the computation time and to provide metric scale which is crucial for lidar depth loss. We use submapping to scale the system to large-scale environments captured over long trajectories. We demonstrate the reconstruction system with data from a multi-camera, lidar sensor suite onboard a legged robot, hand-held while scanning building scenes for 600 metres, and onboard an aerial robot surveying a multi-storey mock disaster site-building.
Authors: Yifu Tao , Yash Bhalgat, Lanke Frank Tarimo Fu, Matias Mattamala , Nived Chebrolu, Maurice Fallon
![RA-L/ICRA 2021 - Unified Landmark Tracking for Odometry [Finalist ICRA Best Student Paper]](https://i.ytimg.com/vi/l3XSGIpQP3Q/mqdefault.jpg "RA-L/ICRA 2021 - Unified Landmark Tracking for Odometry [Finalist ICRA Best Student Paper]
RA-L/ICRA 2021
Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry
David Wisth, Marco Camurri, Sandipan Das, Maurice Fallon
Paper link: https://arxiv.org/abs/2011.06838
VILENS website: https://ori.ox.ac.uk/labs/drs/vilens-tightly-fused-multi-sensor-odometry/
Abstract:
We present an efficient multi-sensor odometry system
for mobile platforms that jointly optimizes visual, lidar, and
inertial information within a single integrated factor graph. This
runs in real-time at full framerate using fixed lag smoothing.
To perform such tight integration, a new method to extract 3D
line and planar primitives from lidar point clouds is presented.
This approach overcomes the suboptimality of typical frame-to-
frame tracking methods by treating the primitives as landmarks
and tracking them over multiple scans. True integration of
lidar features with standard visual features and IMU is made
possible using a subtle passive synchronization of lidar and
camera frames. The lightweight formulation of the 3D features
allows for real-time execution on a single CPU. Our proposed
system has been tested on a variety of platforms and scenarios,
including underground exploration with a legged robot and
outdoor scanning with a dynamically moving handheld device,
for a total duration of 96 min and 2.4 km traveled distance. In
these test sequences, using only one exteroceptive sensor leads
to failure due to either underconstrained geometry (affecting
lidar) or textureless areas caused by aggressive lighting changes
(affecting vision). In these conditions, our factor graph naturally
uses the best information available from each sensor modality
without any hard switches.")
![[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.")
