Author name cluster

Igor Bogoslavskyi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

ICRA Conference 2025 Conference Paper

Adaptive Thresholding for Sequence-Based Place Recognition

Olga Vysotska
Igor Bogoslavskyi
Marco Hutter 0001
Cyrill Stachniss

Robots need to know where they are in the world to operate effectively without human support. One common first step for precise robot localization is visual place recognition. It is a challenging problem, especially when the output is required in an online fashion, and the current state-of-the-art approaches that tackle it usually require either large amounts of labeled training data or rely on parameters that need to be tuned manually, often per dataset. One such parameter often used for sequence-based place recognition is the image similarity threshold that allows to differentiate between pairs of images that represent the same place even in the presence of severe environmental and structural changes, and those that represent different places even if they share a similar appearance. Currently, selecting this threshold is a manual procedure and requires human expertise. We propose an automatic similarity threshold selection technique and integrate it into a complete sequence-based place recognition system. The experiments on a broad range of real-world and simulated data show that our approach is capable of matching image sequences under various illumination, viewpoint and underlying structural changes, runs online, and requires no manual parameter tuning while yielding performance comparable to a manual, dataset-specific parameter tuning. Thus, this paper substantially increases the ease of use of visual place recognition in real-world settings.

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ICRA Conference 2024 Conference Paper

Fast and Robust Normal Estimation for Sparse LiDAR Scans

Igor Bogoslavskyi
Konstantinos Zampogiannis
Raymond Phan

Light Detection and Ranging (LiDAR) technology has proven to be an important part of many robotics systems. Surface normals estimated from LiDAR data are commonly used for a variety of tasks in such systems. As most of the today’s mechanical LiDAR sensors produce sparse data, estimating normals from a single scan in a robust manner poses difficulties. In this paper, we address the problem of estimating normals for sparse LiDAR data avoiding the typical issues of smoothing out the normals in high curvature areas. Mechanical LiDARs rotate a set of rigidly mounted lasers. One firing of such a set of lasers produces an array of points where each point’s neighbor is known due to the known firing pattern of the scanner. We use this knowledge to connect these points to their neighbors and label them using the angles of the lines connecting them. When estimating normals at these points, we only consider points with the same label as neighbors. This allows us to avoid estimating normals in high curvature areas. We evaluate our approach on various data, both self-recorded and publicly available, acquired using various sparse LiDAR sensors. We show that using our method for normal estimation leads to normals that are more robust in areas with high curvature which leads to maps of higher quality. We also show that our method only incurs a constant factor runtime overhead with respect to a lightweight baseline normal estimation procedure and is therefore suited for operation in computationally demanding environments.

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ICRA Conference 2018 Conference Paper

A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

Bartolomeo Della Corte
Igor Bogoslavskyi
Cyrill Stachniss
Giorgio Grisetti

The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple different cues. We provide examples for registering RGBD as well as 3D LIDAR data. In contrast to popular point cloud registration approaches such as ICP our method does not rely on explicit data association and exploits multiple modalities such as raw range and image data streams. Color, depth, and normal information are handled in an uniform manner and the registration is obtained by minimizing the pixel-wise difference between two multi-channel images. We developed a flexible and general framework and implemented our approach inside that framework. We also released our implementation as open source C++ code. The experiments show that our approach allows for an accurate registration of the sensor data without requiring an explicit data association or model-specific adaptations to datasets or sensors. Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.

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IROS Conference 2017 Conference Paper

Analyzing the quality of matched 3D point clouds of objects

Igor Bogoslavskyi
Cyrill Stachniss

3D laser scanners are frequently used sensors for mobile robots or autonomous cars and they are often used to perceive the static as well as dynamic aspects in the scene. In this context, matching 3D point clouds of objects is a crucial capability. Most matching methods such as numerous flavors of ICP provide little information about the quality of the match, i. e. how well do the matched objects correspond to each other, which goes beyond point-to-point or point-to-plane distances. In this paper, we propose a projective method that yields a probabilistic measure for the quality of matched scans. It not only considers the differences in the point locations but can also take free-space information into account. Our approach provides a probabilistic measure that is meaningful enough to evaluate scans and to cluster real-world data such as scans taken with Velodyne scanner in urban scenes in an unsupervised manner.

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IROS Conference 2016 Conference Paper

Fast range image-based segmentation of sparse 3D laser scans for online operation

Igor Bogoslavskyi
Cyrill Stachniss

Object segmentation from 3D range data is an important topic in mobile robotics. A robot navigating in a dynamic environment needs to be aware of objects that might change or move. A segmentation of the laser scans into individual objects is typically the first processing step before a further analysis is performed. In this paper, we present a fast method that segments 3D range data into different objects, runs online, and has small computational demands. Our approach avoids the explicit computation of the 3D point cloud and performs all computations directly on a 2D range image, which enables a fast segmentation for each scan. A further relevant aspect of our method is that we can segment objects even if the 3D data is sparse. This is important for scanners such as the new Velodyne Puck. We implemented our approach in C++ and ROS and thoroughly tested it using different 3D scanners. Our method can operate at over 100 Hz for the 64-beam Velodyne scanner on a single core of a mobile CPU while producing high quality segmentation results. In addition to this, we make the source code for the approach available.

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ICRA Conference 2016 Conference Paper

Robust homing for autonomous robots

Igor Bogoslavskyi
Mladen Mazuran
Cyrill Stachniss

In autonomous exploration tasks, robots usually rely on a SLAM system to build a map of the environment online and then use it for navigation purposes. Although there has been substantial progress in robustly building accurate maps, these systems cannot guarantee the consistency of the resulting environment model. In this paper, we address the problem of robustly guiding a robot back to its starting location after exploring an unknown environment-even if the mapping system fails to produce a consistent map. To tackle this problem, we propose a two-step procedure. First, we check if the current map is consistent using a statistical test. If the map is consistent, we navigate the robot back to its starting location using a standard navigation system. In case of an inconsistent map, however, we propose to rewind the trajectory from the current location to the start without relying on a map. We implemented the proposed system in ROS and showcase its effectiveness on an autonomous exploration robot in real underground and office environments.

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ICRA Conference 2015 Conference Paper

Where to park? minimizing the expected time to find a parking space

Igor Bogoslavskyi
Luciano Spinello
Wolfram Burgard
Cyrill Stachniss

Quickly finding a free parking spot that is close to a desired target location can be a difficult task. This holds for human drivers and autonomous cars alike. In this paper, we investigate the problem of predicting the occupancy of parking spaces and exploiting this information during route planning. We propose an MDP-based planner that considers route information as well as the occupancy probabilities of parking spaces to compute the path that minimizes the expected total time for finding an unoccupied parking space and for walking from the parking location to the target destination. We evaluated our system on real world data gathered over several days in a real parking lot. We furthermore compare our approach to three parking strategies and show that our method outperforms the alternative behaviors.

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