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.

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.

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.

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).

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.

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 LanguageTM(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.

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.

This paper proposes a machine vision approach for plant classification without segmentation and its application in agriculture. Our system can discriminate crop and weed plants growing in commercial fields where crop and weed grow close together and handles overlap between plants. Automated crop / weed discrimination enables weed control strategies with specific treatment of weeds to save cost and mitigate environmental impact. Instead of segmenting the image into individual leaves or plants, we use a Random Forest classifier to estimate crop/weed certainty at sparse pixel positions based on features extracted from a large overlapping neighborhood. These individual sparse results are spatially smoothed using a Markov Random Field and continuous crop/weed regions are inferred in full image resolution through interpolation. We evaluate our approach using a dataset of images captured in an organic carrot farm with an autonomous field robot under field conditions. Applying the plant classification system to images from our dataset and performing cross-validation in a leave one out scheme yields an average classification accuracy of 93.8 %.

In the domain of agricultural robotics, one major application is crop scouting, e.g., for the task of weed control. For this task a key enabler is a robust detection and classification of the plant and species. Automatically distinguishing between plant species is a challenging task, because some species look very similar. It is also difficult to translate the symbolic high level description of the appearances and the differences between the plants used by humans, into a formal, computer understandable form. Also it is not possible to reliably detect structures, like leaves and branches in 3D data provided by our sensor. One approach to solve this problem is to learn how to classify the species by using a set of example plants and machine learning methods. In this paper we are introducing a method for distinguishing plant species using a 3D LIDAR sensor and supervised learning. For that we have developed a set of size and rotation invariant features and evaluated experimentally which are the most descriptive ones. Besides these features we have also compared different learning methods using the toolbox Weka. It turned out that the best methods for our application are simple logistic regression functions, support vector machines and neural networks. In our experiments we used six different plant species, typically available at common nurseries, and about 20 examples of each species. In the laboratory we were able to identify over 98% of these plants correctly.

Determining the physical location is a fundamental challenge in location based services and service robotics. This paper presents an approach to simultaneously determine the pose of a mobile robot and the positions of static wireless nodes based on a new technique to compute the angle of arrival of radio signals. For the overall localization process an Extended Kalman Filter is employed. For the initialization of the nodes positions a Particle Filter is used, to overcome the nonlinearity problem. Experiments show a mean accuracy for the position of the robot of 39mm for an indoor office environment and 80mm for an outdoor landscape environment.

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.

Future 3D television critically relies on mechanisms for automatically acquiring and visualizing high quality 3D content of both indoor and outdoor scenes. The envisioned goal is that a photo-realistic 3D real-time rendering from the actual and potentially arbitrary viewpoint of the beholder who is watching 3DTV becomes possible. Such scenes include movie sets in studios, e.g., for talk shows, TV series and blockbuster movies, but also outdoor scenes, e.g., buildings in a neighborhood for a car chase or cultural heritage sites for a documentary. The goal of 3D model acquisition is to provide the 3D background models where potential 3D actors can be embedded. We present both the 3D acquisition and semi-immersive 3D visualization to give an impression how a future 3D television system could be like.

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

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.

Forest biomass (wood volume) is the most integrative forest structural parameter. Since no remote sensing technique can measure wood volume or biomass directly, a direct biomass determination from air- or spaceborne images always has a regression character. A different approach is to estimate forest biomass indirectly from a forest parameter that can be extracted more accurately than biomass, of which forest height is the closest related one. This makes it possible to utilize remote sensing methods that extract forest heights for biomass assessments: stereoscopic aerial photography, Lidar and Pol-InSAR. In this paper, forest heights were extracted from polarimetric interferometric SAR data (Pol-InSAR) over the spruce dominated test site `Fichtelgebirge'. The extracted heights proved a good correlation with the upper canopy height (h100, top height), and could be converted into forest biomass by means of height-biomass allometry. On this basis, the potential of Pol-InSAR systems for forest biomass estimation is critically discussed, and directions towards a performance optimisation are pointed out

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.

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.