Extrinsic Calibration between a Camera and a 2D Laser Rangefinder using a Photogrammetric Control Field

This study identifies a way to accurately estimate extrinsic calibration parameters between stereo vision camera and 2D laser range finder (LRF) based on 3D reconstruction of monochromatic calibration board and geometric co-planarity constraints between the views from these two sensors. It supports automatic extraction of plane-line correspondences between camera and LRF using monochromatic board, which is further improved by selecting optimal threshold values for laser scan dissection to extract line features from LRF data. Calibration parameters are then obtained by solving co-planarity constraints between the estimated plane and line. Furthermore, the obtained parameters are refined by minimising reprojection error and error from the co-planarity constraints. Moreover, calibration accuracy is achieved because of extraction of reliable plane-line correspondence using monochromatic board which reduces the impact of range-reflectivity-bias observed in LRF data on checkerboard . As the proposed method supports to automatically extract feature correspondences, it provides a major reduction in time required from an operator in comparison to manual methods. The performance is validated by extensive experimentation and simulation, and estimated parameters from the proposed method demonstrate better accuracy than conventional methods.

Extrinsic calibration of a camera and a 2D laser range finder using ping pong balls and the corner of a room

This paper presents a stochastic map building method for mobile robot using a 2-D laser range finder. Unlike other methods that are based on a set of geometric primitives, the presented method builds a map with a set of obstacle regions. In building a map of the environment, the presented algorithm represents the obstacles with a number of stochastic obstacle regions, each of which is characterized by its own stochastic parameters such as mean and covariance. Whereas the geometric primitives based map sometimes does not fit well to sensor data, the presented method reliably represents various types of obstacles including those of irregular walls and sets of tiny objects. Their shapes and features are easily extracted from the stochastic parameters of their obstacle regions, and are used to develop reliable navigation and obstacle avoidance algorithms. The algorithm updates the world map in real time by detecting the changes of each obstacle region. Consequently, it is adequate for modeling the quasi-static environment, which includes occasional changes in positions of the obstacles rather than constant dynamic moves of the obstacles. The presented map building method has successfully been implemented and tested on the ARES-II mobile robot system equipped with a LADAR 2D-laser range finder.

<p style='text-indent:20px;'>The calibration of a 3D laser rangefinder (LRF) and a camera is a key technique in the field of computer vision and intelligent robots. This paper proposes a new method for the calibration of a 3D LRF and a camera based on optimization solution. The calibration is achieved by freely moving a checkerboard pattern in front of the camera and the 3D LRF. The images and the 3D point clouds of the checkerboard pattern in various poses are collected by the camera and the 3D LRF respectively. By using the images, the intrinsic parameters and the poses of the checkerboard pattern are obtained. Then, two kinds of geometric constraints, line-to-plane constraints and plane-to-plane constraints, are constructed to solve the extrinsic parameters by linear optimization. Finally, the intrinsic and extrinsic parameters are further refined by global optimization, and are used to compute the geometric mapping relationship between the 3D LRF and the camera. The proposed calibration method is evaluated with both synthetic data and real data. The experimental results show that the proposed calibration method is accurate and robust to noise.

Many mobile robots nowadays use thermal imaging cameras (TICs) in order to enhance the environment model that is created during exploration tasks. In conventional thermography, thermal images always have to be carefully revised by human operators, which is not practicable in autonomous applications. Unknown surface emissivities are the main source of misinterpretations in thermal images. In this work, we present two methods dealing with these misinterpretations by exploiting the TIC’s changing point of view. While the first approach classifies the regarded material in order to estimate improved surface temperature values, the second one is capable of detecting and removing thermal reflections. The spatial relationship between the thermal images and the regarded surface is made by using a rigidly mounted sensor stack consisting of a TIC and a 3D laser range finder, whose extrinsic calibration is described. During evaluation, we demonstrate the functionality of both approaches.

Robot map navigation and localization are challenging tasks that require the solving of the data association problem for local and global features. Data fusion allows the advantages of two or more sensors to be combined, and complementary cooperation can be obtained. This paper presents two methods to embed depth information in omnidirectional images using the extrinsic calibration of a 2D laser range finder and a central catadioptric camera. The methods presented do not require a visible laser beam, but they assume the planar checkerboard patterns are visible for both the catadioptric camera and the 2D laser range finder. Unlike other approaches, the methods proposed used an invisible laser trace, and they are evaluated at pixel error level using ground truth data from the calibration patterns projected in the omnidirectional image. Results include a mean square error analysis of all calibration poses, and laser point projection on indoor omnidirectional images. We think that embedding range information in omnidirectional images is an interesting tool for data fusion approaches, which can be used in robot map building and localization.

In this paper, we present a real-time 3D robotic indoor mapping system on a wheeled mobile robot equipped with two 2D laser range finder (LRF). We propose the fusion approach of two fixed 2D laser range finder (one horizontal and one vertical), odometers and gyroscope, to build accurate 2D and 3D maps at the same time. The two LRFs perform line-feature based SLAM in horizontal and vertical planes when the robot walking forward. Combined with the pose computed by the information of odometers and gyroscope, we achieve an accurate localization. To represent the 3D environment an octree based map is used. Mapping experiment of a 180m+ corridor was tested on an average speed of 0.5m/s without stop. The results shows the efficiency and accuracy of the 3D mapping system.

This paper presents a novel extrinsic calibration algorithm between a binocular stereo vision system and a 2D LIDAR (laser range finder). Extrinsic calibration of these heterogeneous sensors is required to fuse information obtained separately by vision sensor and LIDAR in the context of intelligent vehicle. By placing a planar chessboard at different positions and orientations in front of the sensors, the proposed method solves the problem based on 3D reconstruction of the chessboard and geometric constraints between views from the stereovision system and the LIDAR. The three principle steps of the approach are: 3D corner points triangulation, 3D plane least-squares estimation, solving extrinsic parameters by applying a non-linear optimization algorithm based on the geometric constraints. To evaluate the performance of the algorithm, experiments based on computer simulation and real data are performed. The proposed approach is also compared with a popular calibration method to show its advantages.

Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to calibrate LRFs based on a corner composed of three mutually perpendicular planes. In contrast to other methods that require a special pattern or assistance from other sensors, the trihedron corner needed in this method is common in daily environments. In practice, we can adjust the position of the LRFs to observe the corner until the laser scanning plane intersects with three planes of the corner. Then, we formed a Perspective-Three-Point problem to solve the position and orientation of each LRF at the common corner coordinate system. The method was validated with synthetic and real experiments, showing better performance than existing methods.

레이저 거리센서와 비전 카메라의 정보를 융합하기 위해서는 두 센서 사이의 상대적인 위치관계를 설명하는 외부 파라미터를 정확하게 보정하는 것이 필수적이다. 본 논문에서는 레이저 거리센서와 카메라 좌표계 간의 외부 파라미터를 기존에 알려진 방법보다 쉬우면서도 정확하게 획득할 수 있는 새로운 보정 방법을 제안하고자 한다. 본 논문에서 제안한 방법의 접근법은 레이저 거리센서로 획득한 3차원 구조물의 모서리 정보를 영상으로 투영하였을 때 반드시 하나의 직선상에 존재해야 한다는 것을 제약조건으로 한다. 이러한 제약조건을 만족하는 3차원 기하모델을 제시하고 이 모델의 에너지 함수를 최소화하기 위한 수치적 해법을 소개한다. 또한 높은 정밀도의 보정을 위하여 레이저 거리정보 및 카메라 영상의 획득 과정에 대해서도 상세히 설명한다. 실험을 통해 제안된 방법의 성능이 기존의 방법에 비하여 보다 높은 정밀도를 보임을 확인할 수 있었다. For data fusion of laser range finder (LRF) and vision camera, accurate calibration of external parameters which describe relative pose between two sensors is necessary. This paper proposes a new calibration method which can acquires more accurate external parameters between a LRF and a vision camera compared to other existing methods. The main motivation of the proposed method is that any corner data of a known 3D structure which is acquired by the LRF should be projected on a straight line in the camera image. To satisfy such constraint, we propose a 3D geometric model and a numerical solution to minimize the energy function of the model. In addition, we describe the implementation steps of the data acquisition of LRF and camera images which are necessary in accurate calibration results. In the experiment results, it is shown that the performance of the proposed method are better in terms of accuracy compared to other conventional methods.

Organic photovoltaics (OPVs) show great promise for both outdoor and indoor applications. However, there remains a lack of understanding around the stability of OPVs, particularly for indoor applications. In this work, the photostability of the poly[(thiophene)‐alt‐(6,7‐difluoro‐2‐(2‐hexyldecyloxy)quinoxaline)]:2,2′‐((2Z,2′Z)‐((4,4,9,9‐tetrahexyl‐4,9‐dihydro‐s‐indaceno[1,2‐b:5,6‐b′]dithiophene‐2,7‐diyl)bis(methanylylidene))bis(3‐oxo‐2,3‐dihydro‐1H‐indene‐2,1‐diylidene))dimalononitrile blend is investigated for both outdoor and indoor applications. Photostability is found to vary drastically with illumination intensity. Devices under high‐intensity white light‐emitting diode (LED) illumination, with their short‐circuit current density (JSC) matching JSC–EQE for AM1.5 G illumination, lose 42% of their initial performance after 30 days of illumination. Contrastingly, after almost 47 days of illumination devices under 1000 lux white LED illumination show no loss in performance. The poor photostability under 1 sun illumination is linked to the poor photostability of IDIC. Through Raman spectroscopy and mass spectrometry, IDIC is found to suffer from photoisomerization, which detrimentally impacts light absorption and carrier extraction. In this work, it is highlighted that under low light levels, the requirement of intrinsic material photostability may be less stringent.

Many teams participating in robotic competitions achieve localization using a 2D map plotted using adaptive Monte Carlo localization, a robot operating system (ROS) open-source software program. However, outdoor environments often include nonlevel terrain such as slopes. In the indoor environment of multilevel structures, the data representing different levels overlap on the map. These factors can lead to localization failures. To resolve this problem, we develop a software by combining HDL localization, which is an ROS open-source software, with our own program, and use it to achieve localization based on a 3D map. Furthermore, the authors observe the erroneous recognition of a slope as a forward obstacle during a competition event. To resolve this, we propose a method to correct erroneous recognition of obstacles using a 2D laser range finder and 3D map and confirm its validity in an experiment carried out on a slope on a university campus.

Dynamic environment perception is an important component for unmanned ground vehicle navigation. In this paper, we focus on the vehicle detection and tracking with two low cost 2D laser range finders. Firstly, a foreground segmentation method is proposed based on a combination of the time cue and motion cue. The modified laser beam model is proposed for the tilted 2D laser range finder in the foreground segmentation. Then, the vehicle detection and preliminary tracking is realized by the modified measurement model. Finally, a new precise tracking method is proposed to refine the preliminary tracking results. Qualitative and quantitative experiments are carried out in real dynamic environment, and the result shows that the proposed method can detect and track the vehicle accurately.

Autonomous navigation of quadcopters in unstructured indoor environments is a major problem due to the difficulty of reliable position sensing. While outdoor applications can use GPS for reliable localization, working indoors will require the use of either laser range finders or some other sensors. If the indoor scene is unknown to a robot, the task of mapping new areas also becomes a necessity. The two processes are combined and run together in a framework of Simultaneous Localization and Mapping (SLAM). Our work is focused on using onboard cameras for the task of SLAM in an indoor scenario. Vision based techniques that do not use time of flight methods like laser range finders, have the potential to provide a low cost alternative framework for navigation. In this work, localization using a monocular SLAM framework on an unknown and unstructured scene, a cascaded position controller along with a Luenberger observer which can combine the data of Inertial sensors and vision based position to generate a complete velocity feedback for the system have been used. Sensor data fusion using EKF (Extended Kalman Filter) have been performed for scale estimation. The localization algorithm has been implemented on a quadcopter. Finally hovering experiment has been performed in an indoor lab based environment.

Unmanned ground vehicle for facility protection mostly uses model of territory for autonomous navigation. However, modeling of territory using several sensors is highly time consuming and sometimes inefficient for road application. Therefore, an estimation of drivable path based on features of road is required for high speed autonomous navigation on road. In this paper, an estimation method of drivable path using camera and 2D laser rangefinder is proposed. First, a vanishing point is estimated based on image data from CCD camera. Second, a road width is estimated based on range data from 2D laser rangefinder. Finally, the drivable path is estimated by fusing the vanishing point and the road width. The proposed method is tested on both well-structured road and unpaved road like cross-country situation.