Dorit Borrmann

Globally consistent 3D mapping with scan matching

A globally consistent solution to the simultaneous localization and mapping (SLAM) problem in 2D with three degrees of freedom (DoF) poses was presented by Lu and Milios [F. Lu, E. Milios, Globally consistent range scan alignment for environment mapping, Autonomous Robots 4 (April) (1997) 333-349]. To create maps suitable for natural environments it is however necessary to consider the 6DoF pose case, namely the three Cartesian coordinates and the roll, pitch and yaw angles. This article describes the extension of the proposed algorithm to deal with these additional DoFs and the resulting non-linearities. Simplifications using Taylor expansion and Cholesky decomposition yield a fast application that handles the massive amount of 3D data and the computational requirements due to the 6DoF. Our experiments demonstrate the functionality of estimating the exact poses and their covariances in all 6DoF, leading to a globally consistent map. The correspondences between scans are found automatically by use of a simple distance heuristic.

Efficient processing of large 3D point clouds

Autonomous robots equipped with laser scanners acquire data at an increasingly high rate. Registration, data abstraction and visualization of this data requires the processing of a massive amount of 3D data. The increasing sampling rates make it easy to acquire Billions of spatial data points. This paper presents algorithms and data structures for handling this data. We propose an efficient octree to store and compress 3D data without loss of precision. We demonstrate its usage for fast 3D scan matching and shape detection algorithms. We evaluate our approach using typical data acquired by mobile scanning platforms.

A mobile robot based system for fully automated thermal 3D mapping

Display Omitted We propose a fully autonomous system for 3D thermal modeling of buildings.A robot finds the positions for data acquisition using 3D sensor placement planning.Data from a laser scanner, a thermal camera, and a photo camera are automatically joined into one full model.Post-processing prepares the data for inspection in a viewer and points out interesting parts in the environment to experts. It is hard to imagine living in a building without electricity and a heating or cooling system these days. Factories and data centers are equally dependent on a continuous functioning of these systems. As beneficial as this development is for our daily life, the consequences of a failure are critical. Malfunctioning power supplies or temperature regulation systems can cause the close-down of an entire factory or data center. Heat and air conditioning losses in buildings lead to a large waste of the limited energy resources and pollute the environment unnecessarily. To detect these flaws as quickly as possible and to prevent the negative consequences constant monitoring of power lines and heat sources is necessary. To this end, we propose a fully automatic system that creates 3D thermal models of indoor environments. The proposed system consists of a mobile platform that is equipped with a 3D laser scanner, an RGB camera and a thermal camera. A novel 3D exploration algorithm ensures efficient data collection that covers the entire scene. The data from all sensors collected at different positions is joined into one common reference frame using calibration and scan matching. In the post-processing step a model is built and points of interest are automatically detected. A viewer is presented that aids experts in analyzing the heat flow and localizing and identifying heat leaks. Results are shown that demonstrate the functionality of the system.

The Project ThermalMapper - Thermal 3D Mapping of Indoor Environments for Saving Energy

Abstract Heat and air conditioning losses in buildings and factories lead to a large amount of wasted energy. The Action Plan for Energy Efficiency of the Commission of the European Communities (2008) estimates that the largest cost-effective energy savings potential lies in residential (≈ 27%) and commercial (≈ 30%) buildings. Imagine a technology that creates a precise digital 3D model of heat distribution and heat flow enabling one to detect all sources of wasted energy and to modify buildings to reach these savings. This paper presents our overall approach to map indoor environments with thermal data in 3D.

Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms

Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets.

Thermal 3D Mapping of Building Façades

Never before in history were humans as dependant on energy as we are today. But the natural ressources are limited and a waste of energy has drastic influences on the environment. In their Action Plan for Energy Efficiency [6] the European Commission estimates that the largest and cost-effictive energy savings potential lies in residential (≈ 27%) and commercial (≈ 30%) buildings. To eliminate heat and air conditioning losses in buildings and factories heat and air leaks need to be localized and identified. Imagine the availability of a complete 3D model of every building that architects can use to analyze the heat insulation of buildings and to identify necessary modifications. In these 3D models temperature peaks are not only detectable but also their extent is visible. A robot equiped with a 3D laser scanner, a thermal camera, and a color camera constitutes the basis for our approach. The data from all three sensors and from different locations are joined into one high-precise 3D model that shows the heat distribution. This paper describes the setup of the hardware and the methods applied to create the 3D model, including the automatic co-calibration of the sensors. Challenges unique to the task of thermal mapping of outdoor environments are discussed.

Full Wave Analysis in 3D laser scans for vegetation detection in urban environments

This paper presents a novel technique for detecting vegetation of virtually all forms in terrestrial laser scanning data of urban environments. We make use of a modern laser range finder capability to measure multiple echoes per laser pulse via Full Wave Analysis. The algorithm is able to efficiently, i.e., less than acquisition time, identify vegetation to a high degree of accuracy (more than 99 percent). We present and evaluate three alternatives to classify candidate regions as either vegetation or non-vegetation.

Mutual Calibration for 3D Thermal Mapping

Abstract Three-dimensional digital heat distribution maps are needed to assess the energy efficiency of real estates. The availability of such maps are of great importance for reducing the ecological footprint of houses, buildings, and factories. Designing estates has reached the point, where so-called Passivhaus buildings make extensive use of the intrinsic heat from internal sources such as waste heat from lighting, white goods, and other electrical devices, but without using dedicated heaters. In our approach for creating high-precise heat distribution maps a robot is equipped with a 3D laser scanner, a thermal camera, and a color camera. Data from all the sensors are combined to model the environment precisely. This paper describes the setup of the sensors and the processing of the acquired data, including the automatic co-calibration needed to fulfill this task.

Evaluation of a Backpack-Mounted 3D Mobile Scanning System

Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario.

Point guards and point clouds: solving general art gallery problems

In this video, we illustrate how one of the classical areas of computational geometry has gained in practical relevance, which in turn gives rise to new, fascinating geometric problems. In particular, we demonstrate how the robot platform IRMA3D can produce high-resolution, virtual 3D environments, based on a limited number of laser scans. Computing an optimal set of scans amounts to solving an instance of the Art Gallery Problem (AGP): Place a minimum number of stationary guards in a polygonal region P, such that all points in P are guarded.