Search Results for author:
Found 11 papers, 7 papers with code
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.
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.
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.
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.
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.
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.
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 Camera Miscalibration Detection
Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications.
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.
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.
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.
