5 research outputs found
Augmenting Visual Place Recognition with Structural Cues
In this paper, we propose to augment image-based place recognition with
structural cues. Specifically, these structural cues are obtained using
structure-from-motion, such that no additional sensors are needed for place
recognition. This is achieved by augmenting the 2D convolutional neural network
(CNN) typically used for image-based place recognition with a 3D CNN that takes
as input a voxel grid derived from the structure-from-motion point cloud. We
evaluate different methods for fusing the 2D and 3D features and obtain best
performance with global average pooling and simple concatenation. On the Oxford
RobotCar dataset, the resulting descriptor exhibits superior recognition
performance compared to descriptors extracted from only one of the input
modalities, including state-of-the-art image-based descriptors. Especially at
low descriptor dimensionalities, we outperform state-of-the-art descriptors by
up to 90%.Comment: 8 pages, published in RA-L & IROS 202
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