Tom Duckett

A multilevel relaxation algorithm for simultaneous localization and mapping

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.

Scan Registration for Autonomous Mining Vehicles Using 3D-NDT

Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the ...

Building gas concentration gridmaps with a mobile robot

This paper addresses the problem of mapping the structure of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with gas sensors. By contrast t ...

Fast, On-Line Learning of Globally Consistent Maps

To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.

Learning globally consistent maps by relaxation

Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of drift errors caused by wheel slippage. The paper introduces a fast, online method of learning globally consistent maps, using only local metric information. The approach differs from previous work in that it is computationally cheap, easy to implement and is guaranteed to find a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot, and quantitative performance measures are used to assess the quality of the maps obtained.

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

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.

Dynamic maps for long-term operation of mobile service robots

This paper introduces a dynamic map for mobile robots that adapts continuously over time. It resolves the stability plasticity dilemma (the tradeoff between adaptation to new patterns and preservation of old patterns) by representing the environment over multiple time scales simultaneously (five in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the time scale. Robust statistics are used to interpret the samples. It is shown that this approach can track both stationary and non-stationary elements of the environment, covering the full spectrum of variations from moving objects to structural changes. The method was evaluated in a five-week experiment in a real dynamic environment. Experimental results show that the resulting map is stable, improves its quality over time and adapts to changes.

Localization for Mobile Robots using Panoramic Vision, Local Features and Particle Filter

In this paper we present a vision-based approach to self-localization that uses a novel scheme to integrate feature-based matching of panoramic images with Monte Carlo localization. A specially modified version of Lowe’s SIFT algorithm is used to match features extracted from local interest points in the image, rather than using global features calculated from the whole image. Experiments conducted in a large, populated indoor environment (up to 5 persons visible) over a period of several months demonstrate the robustness of the approach, including kidnapping and occlusion of up to 90% of the robot’s field of view.

Localization of mobile robots with omnidirectional vision using Particle Filter and iterative SIFT

The Scale Invariant Feature Transform, SIFT, has been successfully applied to robot localization. Still, the number of features extracted with this approach is immense, especially when dealing with omnidirectional vision. In this work, we propose a new approach that reduces the number of features generated by SIFT as well as their extraction and matching time. With the help of a Particle Filter, we demonstrate that we can still localize the mobile robot accurately with a lower number of features.

A Practical Multirobot Localization System

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. In addition, we present the method’s mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera’s intrinsic parameters and hardware’s processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at http://purl.org/robotics/whycon; so, it can be used as an enabling technology for various mobile robotic problems.