REVIEW 1 cited by
LIMO: Lidar-Monocular Visual Odometry
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
LIMO: Lidar-Monocular Visual Odometry
read the original abstract
Higher level functionality in autonomous driving depends strongly on a precise motion estimate of the vehicle. Powerful algorithms have been developed. However, their great majority focuses on either binocular imagery or pure LIDAR measurements. The promising combination of camera and LIDAR for visual localization has mostly been unattended. In this work we fill this gap, by proposing a depth extraction algorithm from LIDAR measurements for camera feature tracks and estimating motion by robustified keyframe based Bundle Adjustment. Semantic labeling is used for outlier rejection and weighting of vegetation landmarks. The capability of this sensor combination is demonstrated on the competitive KITTI dataset, achieving a placement among the top 15. The code is released to the community.
Forward citations
Cited by 1 Pith paper
-
DSP-SLAM++: A Unified Framework for Multi-Class, High-Fidelity Object SLAM in the Wild
DSP-SLAM++ adds asynchronous mapping and fisheye-LiDAR fusion to DSP-SLAM, claiming up to 70% lower object processing latency and real-time performance on 25 Hz multi-class datasets while producing geometrically compl...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.