Mapping of indoor environments by robots using low-cost vision sensors

For robots to operate in human environments they must be able to make their own maps because it is unrealistic to expect a user to enter a map into the robot’s memory; existing floorplans are often incorrect; and human environments tend to change. Traditionally robots have used sonar, infra-red or laser range finders to perform the mapping task. Digital cameras have become very cheap in recent years and they have opened up new possibilities as a sensor for robot perception. Any robot that must interact with humans can reasonably be expected to have a camera for tasks such as face recognition, so it makes sense to also use the camera for navigation. Cameras have advantages over other sensors such as colour information (not available with any other sensor), better immunity to noise (compared to sonar), and not being restricted to operating in a plane (like laser range finders). However, there are disadvantages too, with the principal one being the effect of perspective. This research investigated ways to use a single colour camera as a range sensor to guide an autonomous robot and allow it to build a map of its environment, a process referred to as Simultaneous Localization and Mapping (SLAM). An experimental system was built using a robot controlled via a wireless network connection. Using the on-board camera as the only sensor, the robot successfully explored and mapped indoor office environments. The quality of the resulting maps is comparable to those that have been reported in the literature for sonar or infra-red sensors. Although the maps are not as accurate as ones created with a laser range finder, the solution using a camera is significantly cheaper and is more appropriate for toys and early domestic robots.

System identification, estimation and control for a cost effective open-source quadcopter

This paper describes system identification, estimation and control of translational motion and heading angle for a cost effective open-source quadcopter — the MikroKopter. The dynamics of its built-in sensors, roll and pitch attitude controller, and system latencies are determined and used to design a computationally inexpensive multi-rate velocity estimator that fuses data from the built-in inertial sensors and a low-rate onboard laser range finder. Control is performed using a nested loop structure that is also computationally inexpensive and incorporates different sensors. Experimental results for the estimator and closed-loop positioning are presented and compared with ground truth from a motion capture system.

Datasets for the evaluation of multi-sensor perception in natural environments with challenging conditions

This paper presents large, accurately calibrated and time-synchronised datasets, gathered outdoors in controlled environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. It discusses how the data collection process was designed, the conditions in which these datasets have been gathered, and some possible outcomes of their exploitation, in particular for the evaluation of performance of sensors and perception algorithms for UGVs.

Sensor Data Integrity : Multi-Sensor Perception for Unmanned Ground Vehicles

This document describes large, accurately calibrated and time-synchronised datasets, gathered in controlled environmental conditions, using an unmanned ground vehicle equipped with a wide variety of sensors. These sensors include: multiple laser scanners, a millimetre wave radar scanner, a colour camera and an infra-red camera. Full details of the sensors are given, as well as the calibration parameters needed to locate them with respect to each other and to the platform. This report also specifies the format and content of the data, and the conditions in which the data have been gathered. The data collection was made in two different situations of the vehicle: static and dynamic. The static tests consisted of sensing a fixed ’reference’ terrain, containing simple known objects, from a motionless vehicle. For the dynamic tests, data were acquired from a moving vehicle in various environments, mainly rural, including an open area, a semi-urban zone and a natural area with different types of vegetation. For both categories, data have been gathered in controlled environmental conditions, which included the presence of dust, smoke and rain. Most of the environments involved were static, except for a few specific datasets which involve the presence of a walking pedestrian. Finally, this document presents illustrations of the effects of adverse environmental conditions on sensor data, as a first step towards reliability and integrity in autonomous perceptual systems.

Error modeling and calibration of exteroceptive sensors for accurate mapping applications

Reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. In field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. This is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. The whole system is as reliable as the weakest link in this chain. In this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as LIDAR or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. The coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that captures the dominant geometric and temporal sources of mapping error. This allows the mapping accuracy to be calculated at run time. A generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. This allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. The mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. The techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. Consequently, they should be employed whenever maps are formed from range-based exteroceptive sensor data. © 2009 Wiley Periodicals, Inc.

The Marulan data sets: multi-sensor perception in a natural environment with challenging conditions

In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Evaluation of cooperative exploration strategies using full system simulation

This paper compares different state-of-the-art exploration strategies for teams of mobile robots exploring an unknown environment. The goal is to help in determining a best strategy for a given multi-robot scenario and optimization target. Experiments are done in a 2D-simulation environment with 5 robots that are equipped with a horizontal laser range finder. Required components like SLAM, path planning and obstacle avoidance of every robot are included in a full-system simulation. To evaluate different strategies the time to finish exploration, the number of measurements that have been integrated into the map and the development in size of the explored area over time are used. The results of extensive test runs on three environments with different characteristics show that simple strategies can perform fairly well in many situations but specialized strategies can improve performance with regards to their targeted evaluation measure.

适合于小飞机防撞系统的机载小型激光测距仪

报道了一种重量轻、功耗低、适合于小飞机防撞系统应用的小型激光测距仪。系统基于脉冲激光测距原理，采用905nm半导体脉冲激光器、电感升压式偏置高压电源和可编程逻辑器件（PLD），研制出重量不大于100g，功耗不大于625mW，测量范围100m，盲区3．0m，分辨率±1m的机载小型激光测距仪。实验测试结果表明，其各项技术性能指标符合无人驾驶小飞机防撞系统的应用要求。

用于机载激光测深系统的时间间隔测量模块

机载海洋激光测深系统不仅要求精确测量激光从海表的入射点到海底目标点之间的距离，还要精确测定飞机到海表入射，点之间的距离，为水深数据的海浪校正而确定平均海平面以及波高。为此设计和研制了一高精度时间间隔测量器，该单元基于专用时间数字转换芯片开发，采用延迟线插入法技术，双通道工作，具有高时间分辨率(最高可达250ps)和高测量重复率的特性。给出硬件和软件设计方法以及单元的测试结果。

目标反射特性对激光测距的影响

针对非合作小目标激光测距系统,目标表面的反射特征对激光回波信号有很大的影响。建立测量表面双向反射分布函数(BRDF)的装置,对常用的两种热控材料——白漆涂层和F36多包层,测量了其在1064 nm波长下的双向反射分布函数。得出了白漆涂层镜面反射很小,散射角较大,利于各方向接收回波信号;而F36多包层镜面反射很强,散射角-2°~2°,不利于探测。通过由表面BRDF与由朗伯散射计算得到的最小接收功率的比较,得出了入射角大于45°入射白漆涂层时,回波信号较小;大于2°入射F36多包层时,没有回波信号。

移动机器人基于激光测距和单目视觉的室内同时定位和地图构建

该文研究了部分结构化室内环境中自主移动机器人同时定位和地图构建问题.基于激光和视觉传感器模型的不同,加权最小二乘拟合方法和非局部最大抑制算法被分别用于提取二维水平环境特征和垂直物体边缘.为完成移动机器人在缺少先验地图支持的室内环境中的自主导航任务,该文提出了同时进行扩展卡尔曼滤波定位和构建具有不确定性描述的二维几何地图的具体方法.通过对应用于SmartROB-2移动机器人平台所获得的实验结果和数据的分析讨论,论证了所提出方法的有效性和实用性.

Evaluación experimental del dispositivo LRF HOKUYO URG-04LX-UG01

En robótica móvil existen diferentes dispositivos que permiten percibir la configuración del entorno. Pueden utilizarse alternativas de gran alcance como por ejemplo los ultrasonidos, pero que tienen la desventaja de consumir un tiempo elevado en la realización de las medidas. En corta distancia destacan los sensores basados en la emisión de luz infrarroja, que responden a muy alta velocidad pero tienen muy poco alcance. La obtención de fotografia, en incluso video, por medio de camaras, permite obtener mucha información del entorno, pero exige un procesado normalmente muy elaborado. Los “Laser Range Finder” son dispositivos basados en la emisión de un haz laser que responden a muy alta velocidad en el entorno de unos cuantos metros alrededor del robot móvil, lo que los hacen especialmente adecuados para un uso continuo que permita obtener de forma rapida un mapa de los obstaculos mas próximos. En el presente proyecto se va a realizar un ejercicio de medida con el laser range finder URG-04LX-UG01 para confirmar su utilidad en el ambito de la robótica móvil. ABSTRACT In mobile robotics there are different devices that allow sense the environment configuration. Powerful alternatives may be used as e.g. ultrasounds, but they have the disadvantage of consuming a large time to perform measurements. In short range highlights the infrared light based sensors, that responds at very high speed but have very low range. The photography obtaining, even video, by cameras, allow acquire many environmental information but normally require a very elaborate processing. The Laser Range Finder are devices based on laser beam broadcasting that respond a very high speed in the vicinity of a few meters around the mobile robot, which make them especially suitable for the continuous use, that allows fast obtain of the nearests obstacles map. In this project we are going to do an measurement exercise with laser range finder URG-04LX-UG01 to confirm its utility in mobile robotics scope.

Fast line, arc/circle and leg detection from laser scan data in a player driver

A feature detection system has been developed for real-time identification of lines, circles and people legs from laser range data. A new method sutable for arc/circle detection is proposed: the Inscribed Angle Variance (IAV). Lines are detected using a recursive line fitting method.

Retrievals of thick cloud optical depth from the Geoscience Laser Altimeter System (GLAS) by calibration of solar background signal

Laser beams emitted from the Geoscience Laser Altimeter System (GLAS), as well as other spaceborne laser instruments, can only penetrate clouds to a limit of a few optical depths. As a result, only optical depths of thinner clouds (< about 3 for GLAS) are retrieved from the reflected lidar signal. This paper presents a comprehensive study of possible retrievals of optical depth of thick clouds using solar background light and treating GLAS as a solar radiometer. To do so one must first calibrate the reflected solar radiation received by the photon-counting detectors of the GLAS 532-nm channel, the primary channel for atmospheric products. Solar background radiation is regarded as a noise to be subtracted in the retrieval process of the lidar products. However, once calibrated, it becomes a signal that can be used in studying the properties of optically thick clouds. In this paper, three calibration methods are presented: (i) calibration with coincident airborne and GLAS observations, (ii) calibration with coincident Geostationary Opera- tional Environmental Satellite (GOES) and GLAS observations of deep convective clouds, and (iii) cali- bration from first principles using optical depth of thin water clouds over ocean retrieved by GLAS active remote sensing. Results from the three methods agree well with each other. Cloud optical depth (COD) is retrieved from the calibrated solar background signal using a one-channel retrieval. Comparison with COD retrieved from GOES during GLAS overpasses shows that the average difference between the two retriev- als is 24%. As an example, the COD values retrieved from GLAS solar background are illustrated for a marine stratocumulus cloud field that is too thick to be penetrated by the GLAS laser. Based on this study, optical depths for thick clouds will be provided as a supplementary product to the existing operational GLAS cloud products in future GLAS data releases.

A clinically applicable laser-based image-guided system for laparoscopic liver procedures

PURPOSE Laser range scanners (LRS) allow performing a surface scan without physical contact with the organ, yielding higher registration accuracy for image-guided surgery (IGS) systems. However, the use of LRS-based registration in laparoscopic liver surgery is still limited because current solutions are composed of expensive and bulky equipment which can hardly be integrated in a surgical scenario. METHODS In this work, we present a novel LRS-based IGS system for laparoscopic liver procedures. A triangulation process is formulated to compute the 3D coordinates of laser points by using the existing IGS system tracking devices. This allows the use of a compact and cost-effective LRS and therefore facilitates the integration into the laparoscopic setup. The 3D laser points are then reconstructed into a surface to register to the preoperative liver model using a multi-level registration process. RESULTS Experimental results show that the proposed system provides submillimeter scanning precision and accuracy comparable to those reported in the literature. Further quantitative analysis shows that the proposed system is able to achieve a patient-to-image registration accuracy, described as target registration error, of [Formula: see text]. CONCLUSIONS We believe that the presented approach will lead to a faster integration of LRS-based registration techniques in the surgical environment. Further studies will focus on optimizing scanning time and on the respiratory motion compensation.