21 research outputs found
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping
We present a novel 3D mapping method leveraging the recent progress in neural
implicit representation for 3D reconstruction. Most existing state-of-the-art
neural implicit representation methods are limited to object-level
reconstructions and can not incrementally perform updates given new data. In
this work, we propose a fusion strategy and training pipeline to incrementally
build and update neural implicit representations that enable the reconstruction
of large scenes from sequential partial observations. By representing an
arbitrarily sized scene as a grid of latent codes and performing updates
directly in latent space, we show that incrementally built occupancy maps can
be obtained in real-time even on a CPU. Compared to traditional approaches such
as Truncated Signed Distance Fields (TSDFs), our map representation is
significantly more robust in yielding a better scene completeness given noisy
inputs. We demonstrate the performance of our approach in thorough experimental
validation on real-world datasets with varying degrees of added pose noise.Comment: 3DV 2021. Equal contribution between the first two authors. Code:
https://github.com/ethz-asl/neuralblo
Local and Global Information in Obstacle Detection on Railway Tracks
Reliable obstacle detection on railways could help prevent collisions that
result in injuries and potentially damage or derail the train. Unfortunately,
generic object detectors do not have enough classes to account for all possible
scenarios, and datasets featuring objects on railways are challenging to
obtain. We propose utilizing a shallow network to learn railway segmentation
from normal railway images. The limited receptive field of the network prevents
overconfident predictions and allows the network to focus on the locally very
distinct and repetitive patterns of the railway environment. Additionally, we
explore the controlled inclusion of global information by learning to
hallucinate obstacle-free images. We evaluate our method on a custom dataset
featuring railway images with artificially augmented obstacles. Our proposed
method outperforms other learning-based baseline methods
SegMap: 3D Segment Mapping using Data-Driven Descriptors
When performing localization and mapping, working at the level of structure
can be advantageous in terms of robustness to environmental changes and
differences in illumination. This paper presents SegMap: a map representation
solution to the localization and mapping problem based on the extraction of
segments in 3D point clouds. In addition to facilitating the computationally
intensive task of processing 3D point clouds, working at the level of segments
addresses the data compression requirements of real-time single- and
multi-robot systems. While current methods extract descriptors for the single
task of localization, SegMap leverages a data-driven descriptor in order to
extract meaningful features that can also be used for reconstructing a dense 3D
map of the environment and for extracting semantic information. This is
particularly interesting for navigation tasks and for providing visual feedback
to end-users such as robot operators, for example in search and rescue
scenarios. These capabilities are demonstrated in multiple urban driving and
search and rescue experiments. Our method leads to an increase of area under
the ROC curve of 28.3% over current state of the art using eigenvalue based
features. We also obtain very similar reconstruction capabilities to a model
specifically trained for this task. The SegMap implementation will be made
available open-source along with easy to run demonstrations at
www.github.com/ethz-asl/segmap. A video demonstration is available at
https://youtu.be/CMk4w4eRobg