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Peter Biber

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12 papers

1 author row

IROS Conference 2024 Conference Paper

Fast Global Point Cloud Registration using Semantic NDT

Robert Schirmer
Narunas Vaskevicius
Peter Biber
Cyrill Stachniss

Robust and accurate point cloud registration is an essential part of many robotic tasks such as SLAM or object pose retrieval. In this paper, we address the problem of global 3D point cloud registration, i. e. , the task of estimating the 3D rigid body transform between a source and a target point cloud without any initial guess. Typically, the problem is solved by extracting and matching features to find a data association and then computing a transform that minimizes the squared distance between points. Our approach combines the normal distributions transform and oriented point pair framework and introduces the NDT distance histogram to quickly generate and test candidate transforms. Our method further exploits semantic information if available for greater speed. We implement our algorithm in C++ and compare it to other state-of-the-art approaches on a diverse set of environments. Our evaluation shows that our method outperforms the other approaches, especially concerning run-time and compute efficiency.

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IROS Conference 2022 Conference Paper

Detecting Invalid Map Merges in Lifelong SLAM

Matthias Holoch
Gerhard Kurz
Peter Biber

For Lifelong SLAM, one has to deal with temporary localization failures, e. g. , induced by kidnapping. We achieve this by starting a new map and merging it with the previous map as soon as relocalization succeeds. Since relocalization methods are fallible, it can happen that such a merge is invalid, e. g. , due to perceptual aliasing. To address this issue, we propose methods to detect and undo invalid merges. These methods compare incoming scans with scans that were previously merged into the current map and consider how well they agree with each other. Evaluation of our methods takes place using a dataset that consists of multiple flat and office environments, as well as the public MIT Stata Center dataset. We show that methods based on a change detection algorithm and on comparison of gridmaps perform well in both environments and can be run in real-time with a reasonable computational cost.

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IROS Conference 2022 Conference Paper

When Geometry is not Enough: Using Reflector Markers in Lidar SLAM

Gerhard Kurz
Sebastian A. Scherer
Peter Biber
David Fleer

Lidar-based SLAM systems perform well in a wide range of circumstances by relying on the geometry of the environment. However, even mature and reliable approaches struggle when the environment contains structureless areas such as long hallways. To allow the use of lidar-based SLAM in such environments, we propose to add reflector markers in specific locations that would otherwise be difficult. We present an algorithm to reliably detect these markers and two approaches to fuse the detected markers with geometry-based scan matching. The performance of the proposed methods is demonstrated on real-world datasets from several industrial environments.

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IROS Conference 2021 Conference Paper

Geometry-based Graph Pruning for Lifelong SLAM

Gerhard Kurz
Matthias Holoch
Peter Biber

Lifelong SLAM considers long-term operation of a robot where already mapped locations are revisited many times in changing environments. As a result, traditional graph-based SLAM approaches eventually become extremely slow due to the continuous growth of the graph and the loss of sparsity. Both problems can be addressed by a graph pruning algorithm. It carefully removes vertices and edges to keep the graph size reasonable while preserving the information needed to provide good SLAM results. We propose a novel method that considers geometric criteria for choosing the vertices to be pruned. It is efficient, easy to implement, and leads to a graph with evenly spread vertices that remain part of the robot trajectory. Furthermore, we present a novel approach of marginalization that is more robust to wrong loop closures than existing methods. The proposed algorithm is evaluated on two publicly available real-world long-term datasets and compared to the unpruned case as well as ground truth. We show that even on a long dataset (25h), our approach manages to keep the graph sparse and the speed high while still providing good accuracy (40 times speed up, 6cm map error compared to unpruned case).

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IROS Conference 2019 Conference Paper

Better Lost in Transition Than Lost in Space: SLAM State Machine

Mirco Colosi
Sebastian Haug
Peter Biber
Kai O. Arras
Giorgio Grisetti

A Simultaneous Localization and Mapping (SLAM) system is a complex program consisting of several interconnected components with different functionalities such as optimization, tracking or loop detection. Whereas the literature addresses in detail how enhancing the algorithmic aspects of the individual components improves SLAM performance, the modal aspects, such as when to localize, relocalize or close a loop, are usually left aside. In this paper, we address the modal aspects of a SLAM system and show that the design of the modal controller has a strong impact on SLAM performance in particular in terms of robustness against unforeseen events such as sensor failures, perceptual aliasing or kidnapping. We preset a novel taxonomy for the components of a modern SLAM system, investigate their interplay and propose a highly modular architecture of a generic SLAM system using the Unified Modeling Language TM (UML) state machine formalism. The result, called SLAM state machine, is compared to the modal controller of several state-of-the-art SLAM systems and evaluated in two experiments. We demonstrate that our state machine handles unforeseen events much more robustly than the state-of-the-art systems.

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ICRA Conference 2019 Conference Paper

Coverage Path Planning in Belief Space

Robert Schirmer
Peter Biber
Cyrill Stachniss

For safety reasons, robotic lawn mowers and similar devices are required to stay within a predefined working area. Keeping the robot within its workspace is typically achieved by special safeguards such as a wire installed in the ground. In the case of robotic lawn mowers, this causes a certain customer reluctance. It is more desirable to fulfill those safety-critical tasks by safe navigation and path planning. In this paper, we tackle the problem of planning a coverage path composed of parallel lanes that maximizes robot safety under the constraints of cheap, low range sensors and thus substantial uncertainty in the robot's belief and ability to execute actions. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for an uncertainty-aware coverage path while avoiding collisions. We implemented our approach using C++ and ROS and thoroughly tested it on real garden data. The experiment shows that our approach leads to safer meander patterns for the lawn mower and takes expected localizability information into account.

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IROS Conference 2017 Conference Paper

Efficient path planning in belief space for safe navigation

Robert Schirmer
Peter Biber
Cyrill Stachniss

Robotic lawn-mowers are required to stay within a predefined working area, otherwise they may drive into a pond or on the street. This turns navigation and path planning into safety critical components. If we consider using SLAM techniques in that context, we must be able to provide safety guarantees in the presence of sensor/actuator noise and featureless areas in the environment. In this paper, we tackle the problem of planning a path that maximizes robot safety while navigating inside the working area and under the constraints of limited computing resources and cheap sensors. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for a path from start to goal in belief space using an extended heuristic search algorithm. We implemented our approach using C++ and ROS and thoroughly tested it on simulation data recorded on eight different gardens, as well as on a real robot. The experiments presented in this paper show that our approach leads to short computation times and short paths while maximizing robot safety under certain assumptions.

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ICRA Conference 2009 Conference Paper

Radiation pattern correlation for mobile robot localization in low power wireless networks

Juergen Graefenstein
Amos Albert
Peter Biber

We present a new method for localization using received signal strength indicator (RSSI) in ordinary wireless communication networks such as specified by IEEE 802. 15. 4. The method exploits the anisotropy of the antenna gain to determine the bearing of the robot relative to reference radio nodes. This method is not only more precise than the mapping of the RSSI to distance only, it also allows to estimate the orientation of the robot and to monitor the integrity of the measurement. The integrity measure is also incorporated into the RSSI to distance mapping and a thorough error analysis is presented. The paper describes the localization concept and presents experimental results for mobile robot localization in an outdoor environment. The achieved accuracy is significantly increased compared to previously developed RSSI based localization methods.

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ICRA Conference 2006 Conference Paper

nScan-matching: Simultaneous Matching of Multiple Scans and Application to SLAM

Peter Biber
Wolfgang Straßer

Scan matching is a popular way of recovering a mobile robot's motion and constitutes the basis of many localization and mapping approaches. Consequently, a variety of scan matching algorithms have been proposed in the past. All these algorithms share one common attribute: They match pairs of scans to obtain spatial relations between two robot poses. In this paper we present a method for matching multiple scans simultaneously. We discuss the need for such a method and describe how the result of such a multi-scan matching can be incorporated into relation-based SLAM in the Lu and Milios style

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ICRA Conference 2005 Conference Paper

Omnidirectional 3D Modeling on a Mobile Robot using Graph Cuts

Sven Fleck
Florian Busch
Peter Biber
Henrik Andreasson
Wolfgang Straßer

For a mobile robot it is a natural task to build a 3D model of its environment. Such a model is not only useful for planning robot actions but also to provide a remote human surveillant a realistic visualization of the robot’s state with respect to the environment. Acquiring 3D models of environments is also an important task on its own with many possible applications like creating virtual interactive walkthroughs or as basis for 3D-TV. In this paper we present our method to acquire a 3D model using a mobile robot that is equipped with a laser scanner and a panoramic camera. The method is based on calculating dense depth maps for panoramic images using pairs of panoramic images taken from different positions using stereo matching. Traditional 2D-SLAM using laser-scan-matching is used to determine the needed camera poses. To receive high-quality results we use a high-quality stereo matching algorithm – the graph cut method. We describe the necessary modifications to handle panoramic images and specialized post-processing methods.

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IROS Conference 2004 Conference Paper

3D modeling of indoor environments by a mobile robot with a laser scanner and panoramic camera

Peter Biber
Henrik Andreasson
Tom Duckett
Andreas Schilling 0001

We present a method to acquire a realistic, visually convincing 3D model of indoor office environments based on a mobile robot that is equipped with a laser range scanner and a panoramic camera. The data of the 2D laser scans are used to solve the SLAM problem and to extract walls. Textures for walls and floor are built from the images of a calibrated panoramic camera. Multiresolution blending is used to hide seams in the generated textures.

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IROS Conference 2003 Conference Paper

The normal distributions transform: a new approach to laser scan matching

Peter Biber
Wolfgang Straßer

Matching 2D range scans is a basic component of many localization and mapping algorithms. Most scan match algorithms require finding correspondences between the used features, i. e. points or lines. We propose an alternative representation for a range scan, the normal distributions transform. Similar to an occupancy grid, we subdivide the 2D plane into cells. To each cell, we assign a normal distribution, which locally models the probability of measuring a point. The result of the transform is a piecewise continuous and differentiable probability density, that can be used to match another scan using Newton's algorithm. Thereby, no explicit correspondences have to be established. We present the algorithm in detail and show the application to relative position tracking and simultaneous localization and map building (SLAM). First results on real data demonstrate, that the algorithm is capable to map unmodified indoor environments reliable and in real time, even without using odometry data.

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