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Found 11 papers, 7 papers with code
SegMap: 3D Segment Mapping using Data-Driven Descriptors
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.
SegMap: Segment-based mapping and localization using data-driven descriptors
We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds.
Go Fetch: Mobile Manipulation in Unstructured Environments
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations.
Learning Camera Miscalibration Detection
Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications.
Driving Through Ghosts: Behavioral Cloning with False Positives
In this work, we propose a behavioral cloning approach that can safely leverage imperfect perception without being conservative.
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target.
Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping
As a basis for a localization system we propose a complete on-board mapping pipeline able to map robust meaningful landmarks, such as poles from power lines, in the vicinity of the vehicle.
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning
Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration.
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.
Descriptellation: Deep Learned Constellation Descriptors
However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections.
Local and Global Information in Obstacle Detection on Railway Tracks
We evaluate our method on a custom dataset featuring railway images with artificially augmented obstacles.
