Henrik Andreasson

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.

Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations

Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating System (ROS).

Appearance-based loop detection from 3D laser data using the normal distributions transform

We propose a new approach to appearance based loop detection from metric 3D maps, exploiting the NDT surface representation. Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms. We also present a quantitative performance evaluation using two real-world data sets, showing that the proposed method works well in different environments.

Mini-SLAM: Minimalistic Visual SLAM in Large-Scale Environments Based on a New Interpretation of Image Similarity

This paper presents a vision-based approach to SLAM in large-scale environments with minimal sensing and computational requirements. The approach is based on a graphical representation of robot poses and links between the poses. Links between the robot poses are established based on odomety and image similarity, then a relaxation algorithm is used to generate a globally consistent map. To estimate the covariance matrix for links obtained from the vision sensor, a novel method is introduced based on the relative similarity of neighbouring images, without requiring distances to image features or multiple view geometry. Indoor and outdoor experiments demonstrate that the approach scales well to large-scale environments, producing topologically correct and geometrically accurate maps at minimal computational cost. Mini-SLAM was found to produce consistent maps in an unstructured, large-scale environment (the total path length was 1.4 km) containing indoor and outdoor passages.

A Minimalistic Approach to Appearance-Based Visual SLAM

This paper presents a vision-based approach to simultaneous localization and mapping (SLAM) in indoor/outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omnidirectional vision sensor, a novel method is introduced based on the relative similarity of neighboring images. This new method does not require the determination of distances to image features using multiple-view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle different environments (without modification of the parameters), and it can cope with violations of the ldquoflat floor assumptionrdquo to some degree and scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g., for solving the multirobot SLAM problem with unknown initial poses.

Has somethong changed here? Autonomous difference detection for security patrol robots

This paper presents a system for autonomous change detection with a security patrol robot. In an initial step a reference model of the environment is created and changes are then detected with respect to the reference model as differences in coloured 3D point clouds, which are obtained from a 3D laser range scanner and a CCD camera. The suggested approach introduces several novel aspects, including a registration method that utilizes local visual features to determine point correspondences (thus essentially working without an initial pose estimate) and the 3D-NDT representation with adaptive cell size to efficiently represent both the spatial and colour aspects of the reference model. Apart from a detailed description of the individual parts of the difference detection system, a qualitative experimental evaluation in an indoor lab environment is presented, which demonstrates that the suggested system is able register and detect changes in spatial 3D data and also to detect changes that occur in colour space and are not observable using range values only.

Independent Markov chain occupancy grid maps for representation of dynamic environment

In this paper we propose a new grid based approach to model a dynamic environment. Each grid cell is assumed to be an independent Markov chain (iMac) with two states. The state transition parameters are learned online and modeled as two Poisson processes. As a result, our representation not only encodes the expected occupancy of the cell, but also models the expected dynamics within the cell. The paper also presents a strategy based on recency weighting to learn the model parameters from observations that is able to deal with non-stationary cell dynamics. Moreover, an interpretation of the model parameters with discussion about the convergence rates of the cells is presented. The proposed model is experimentally validated using offline data recorded with a Laser Guided Vehicle (LGV) system running in production use.

Path planning in 3D environments using the Normal Distributions Transform

Planning feasible paths in fully three-dimensional environments is a challenging problem. Application of existing algorithms typically requires the use of limited 3D representations that discard potentially useful information. This article proposes a novel approach to path planning that utilizes a full 3D representation directly: the Three-Dimensional Normal Distributions Transform (3D-NDT). The well known wavefront planner is modified to use 3D-NDT as a basis for map representation and evaluated using both indoor and outdoor data sets. The use of 3D-NDT for path planning is thus demonstrated to be a viable choice with good expressive capabilities.

Omnidirectional 3D Modeling on a Mobile Robot using Graph Cuts

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.