Effective Maximum Likelihood Grid Map With Conflict Evaluation Filter Using Sonar Sensors

In this paper, we address the problem of building a grid map using cheap sonar sensors, i.e., the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, which is called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood (ML) approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors and does not require adjustment of parameters for various environments.      

Using mobile beacons to locate sensors in obstructed environments

Locating sensors in an indoor environment is a challenging problem due to the insufficient distance measurements caused by short ultrasound range and the incorrect distance measurements caused by multipath effect of ultrasound. In this paper, we propose a virtual ruler approach, in which a vehicle equipped with multiple ultrasound beacons travels around the area to measure distances between pairwise sensors. Virtual Ruler can not only obtain sufficient distances between pairwise sensors, but can also eliminate incorrect distances in the distance measurement phase of sensor localization. We propose to measure the distance between pairwise sensors from multiple perspectives using the virtual ruler and filter incorrect values through a statistical approach. By assigning measured distances with confidence values, the localization algorithm can intelligently localize each sensor based on high confidence distances, which greatly improves localization accuracy. Our performance evaluation shows that the proposed approach can achieve better localization results than previous approaches in an indoor environment.     

An exploration strategy using sonar sensors in corridor environments

We present a novel solution for topological exploration in corridor environments using cheap and error-prone sonar sensors. Topological exploration requires significant location detection and motion planning. To detect nodes (i.e., significant places) robustly, we propose a new measure, the eigenvalue ratio (EVR), which converts geometrical shapes in the environment into quantitative values using principal component analysis. For planning the safe motion of a robot, we propose the circle following (CF) method, which abstracts the geometry of the environment while taking the characteristics of the sonar sensors into consideration. Integrating the EVR with the CF method results in a topological exploration strategy using sonar sensors approach. The practicality of this approach is demonstrated by simulations and real experiments in corridor environments.     

Heterogeneous multisensor fusion for mapping dynamic environments

In this paper, we propose a heterogeneous multisensor fusion algorithm for mapping in dynamic environments. The algorithm synergistically integrates the information obtained from an uncalibrated camera and sonar sensors to facilitate mapping and tracking. The sonar data is mainly used to build a weighted line-based map via the fuzzy clustering technique. The line weight, with confidence corresponding to the moving object, is determined by both sonar and vision data. The motion tracking is primarily accomplished by vision data using particle filtering and the sonar vectors originated from moving objects are used to modulate the sample weighting. A fuzzy system is implemented to fuse the two sensor data features. Additionally, in order to build a consistent global map and maintain reliable tracking of moving objects, the well-known extended Kalman filter is applied to estimate the states of robot pose and map features. Thus, more robust performance in mapping as well as tracking are achieved. The empirical results carried out on the Pioneer 2DX mobile robot demonstrate that the proposed algorithm outperforms the methods a using homogeneous sensor, in mapping as well as tracking behaviors.     

A Mobile Robot Exploration Strategy with Low Cost Sonar and Tungsten-Halogen Structured Light

Autonomous environment mapping is an essential part of efficiently carrying out complex missions in unknown indoor environments. In this paper, a low cost mapping system composed of a web camera with structured light and sonar sensors is presented. We propose a novel exploration strategy based on the frontier concept using the low cost mapping system. Based on the complementary characteristics of a web camera with structured light and sonar sensors, two different sensors are fused to make a mobile robot explore an unknown environment with efficient mapping. Sonar sensors are used to roughly find obstacles, and the structured light vision system is used to increase the occupancy probability of obstacles or walls detected by sonar sensors. To overcome the inaccuracy of the frontier-based exploration, we propose an exploration strategy that would both define obstacles and reveal new regions using the mapping system. Since the processing cost of the vision module is high, we resolve the vision sensing placement problem to minimize the number of vision sensing in analyzing the geometry of the proposed sonar and vision probability models. Through simulations and indoor experiments, the efficiency of the proposed exploration strategy is proved and compared to other exploration strategies.      

Robust RBPF-SLAM for Indoor Mobile Robots Using Sonar Sensors in Non-Static Environments

We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments.     

Real-time hierarchical stereo Visual SLAM in large-scale environments

In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divides the whole map into local sub-maps identified by the so-called fingerprints (vehicle poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on fingerprints has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep the local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. Some experimental results for different large-scale outdoor environments are presented, showing an almost constant processing time.     

A Fast Firing Binaural System for Ultrasonic Pattern Recognition

A binaural sonar configuration with capability to detect and identify walls, edges and corners on real-time is presented in this work. A new multi-echo ultra-fast firing method increases the sonar acquisition rate, and provides crossed measurements without interference. A feature map is built on-line using Bayesian updating and classification rules. Three classifiers are implemented and analyzed: minimum risk (MR), maximum a posteriori (MAP), and minimum distance (MD). Experimental results of ultrasonic reflector recognition, using data collected in a specular indoor environment are presented in the paper.     

Mapping freshwater marsh species distributions using WorldView-2 high-resolution multispectral satellite imagery

Freshwater wetlands are highly diverse, spatially heterogeneous, and seasonally dynamic systems that present unique challenges to remote sensing. Maximum likelihood and support vector machine-supervised classification were compared to map wetland plant species distributions in a deltaic environment using high-resolution WorldView-2 satellite imagery. The benefits of the sensor’s new coastal blue, yellow, and red-edge bands were tested for mapping coastal vegetation and the eight-band results were compared to classifications performed using band combinations and spatial resolutions characteristic of other available high-resolution satellite sensors. Unlike previous studies, this study found that support vector machine classification did not provide significantly different results from maximum likelihood classification. The maximum likelihood classifier provided the highest overall classification accuracy, at 75%, with user’s and producer’s accuracies for individual species ranging from 0% to 100%. Overall, maximum likelihood classification of WorldView-2 imagery provided satisfactory results for species distribution mapping within this freshwater delta system and compared favourably to results of previous studies using hyperspectral imagery, but at much lower acquisition cost and greater ease of processing. The red-edge and coastal blue bands appear to contribute the most to improved vegetation mapping capability over high-resolution satellite sensors that employ only four spectral bands.     

Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data

Simultaneous Localization and Map building (SLAM) is referred to as the ability of an Autonomous Mobile Robot (AMR) to incrementally extract the surrounding features for estimating its pose in an unknown location and unknown environment. In this paper, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors to address the SLAM problem. The proposed algorithm explicitly initializes and tracks the line (or wall) features from a comparison between two overlapping sensor measurements buffers. The experimental studies on a Pioneer 2DX mobile robot equipped with sonar sensors suggest that SLAM problem can be solved by the proposed algorithm. The estimated trajectory of AMR from the standard model based on Extended Kalman Filter (EKF) localization for the same experiment is also provided for comparison.     

基于ROS与Kinect的移动机器人同时定位与地图构建

同时定位与地图构建(SLAM)技术一直以来都是移动机器人实现自主导航和避障的核心问题,移动机器人需要借助传感器来探测周围的物体同时构建出相应区域的地图。由于传统的1D和2D传感器,如超声波传感器、声呐和激光测距仪等在建图过程中无法检测出Z轴(垂直方向)上的信息,易增加机器人发生碰撞的概率,同时影响建图结果的精确度。本文利用Kinect作为机器人SLAM的传感器,将其采集到的三维信息转化成二维的激光数据进行地图构建,同时借助机器人操作系统(robot operating system,ROS)进行仿真分析和实际测试。结果表明Kinect可以弥补1D和2D传感器采集信息的不足,同时能够较好的保持建图的完整性和可靠性,适用于室内的移动机器人SLAM实现。     

Workspace mapping based on multisensor information fusion using heterogeneous onboard sensors

In recent years, multiple robot systems that perform team operations have been developed. These robot systems are expected to execute complicated tasks smoothly in a given congested workspace. In this article, we propose a workspace mapping algorithm using ultrasonic stereo sonar and an image sensor in order to operate the mobile robots among obstacles. This workspace mapping algorithm involves two steps: (1) the position detection of obstacles using ultrasonic stereo sonar, and (2) the shape detection of obstacles using an image sensor. While each robot moves around in the given workspace, the two steps of the mapping algorithm are repeated and sensor data are collected. The robot measures the distance and the direction of obstacles using ultrasonic stereo sonar. The shape of obstacles is also captured using an onboard image sensor. A workspace map is created based on the sensor data accumulated from the proposed method, and successful results are also obtained through experiments.     

Improved occupancy grid mapping in specular environment

This paper addresses the improved method for sonar sensor modeling which reduces the specular reflection uncertainty in the occupancy grid. Such uncertainty reduction is often required in the occupancy grid mapping where the false sensory information can lead to poor performance. Here, a novel algorithm is proposed which is capable of discarding the unreliable sonar sensor information generated due to specular reflection. Further, the inconsistency estimation in sonar measurement has been evaluated and eliminated by fuzzy rules based model. To achieve the grid map with improved accuracy, the sonar information is further updated by using a Bayesian approach. In this paper the approach is experimented for the office environment and the model is used for grid mapping. The experimental results show 6.6% improvement in the global grid map and it is also found that the proposed approach is consuming nearly 16.5% less computation time as compared to the conventional approach of occupancy grid mapping for the indoor environments.     

Maximum likelihood set for estimating a probability mass function

We propose a new method for estimating the probability mass function (pmf) of a discrete and finite random variable from a small sample. We focus on the observed counts--the number of times each value appears in the sample--and define the maximum likelihood set (MLS) as the set of pmfs that put more mass on the observed counts than on any other set of counts possible for the same sample size. We characterize the MLS in detail in this article. We show that the MLS is a diamond-shaped subset of the probability simplex [0,1]k bounded by at most k x (k-1) hyper-planes, where k is the number of possible values of the random variable. The MLS always contains the empirical distribution, as well as a family of Bayesian estimators based on a Dirichlet prior, particularly the well-known Laplace estimator. We propose to select from the MLS the pmf that is closest to a fixed pmf that encodes prior knowledge. When using Kullback-Leibler distance for this selection, the optimization problem comprises finding the minimum of a convex function over a domain defined by linear inequalities, for which standard numerical procedures are available. We apply this estimate to language modeling using Zipf's law to encode prior knowledge and show that this method permits obtaining state-of-the-art results while being conceptually simpler than most competing methods.     

Heterogeneous Teams of Modular Robots for Mapping and Exploration

In this article, we present the design of a team of heterogeneous, centimeter-scale robots that collaborate to map and explore unknown environments. The robots, called Millibots, are configured from modular components that include sonar and IR sensors, camera, communication, computation, and mobility modules. Robots with different configurations use their special capabilities collaboratively to accomplish a given task. For mapping and exploration with multiple robots, it is critical to know the relative positions of each robot with respect to the others. We have developed a novel localization system that uses sonar-based distance measurements to determine the positions of all the robots in the group. With their positions known, we use an occupancy grid Bayesian mapping algorithm to combine the sensor data from multiple robots with different sensing modalities. Finally, we present the results of several mapping experiments conducted by a user-guided team of five robots operating in a room containing multiple obstacles.     

Run-Time Validation of Speculative Optimizations using CVC.

Translation validation is an approach for validating the output of optimizing compilers. Rather than verifying the compiler itself, translation validation mandates that every run of the compiler generate a formal proof that the produced target code is a correct implementation of the source code. Speculative loop optimizations are aggressive optimizations which are only correct under certain conditions which cannot be validated at compile time. We propose using an automatic theorem prover together with the translation validation framework to automatically generate run-time tests for such speculative optimizations. This run-time validation approach must not only detect the conditions under which an optimization generates incorrect code, but also provide a way to recover from the optimization without aborting the program or producing an incorrect result. In this paper, we apply the run-time validation technique to a class of speculative reordering transformations and give some initial results of run-time tests generated by the theorem prover CVC.     

A nonparametric learning approach to range sensing from omnidirectional vision

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration—tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.     

Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wireless sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.     

Map building through pseudo dense scan matching using visual sonar data

This paper presents a novel approach to the vision based grid map building and localization problem that works in a complex indoor environment with a single forward viewing camera. Most existing visual SLAM has been limited to the feature-based method and only a few researchers have proposed visual SLAM methods for building a grid map using a stereo vision system which has not been popular in practical application. In this paper, we estimate the planar depth by applying a simple visual sonar ranging technique to the single camera image and then associating sequential scans through our own pseudo dense adaptive scan matching algorithm reducing the processing time compared to the standard point-to-point correspondence based algorithm and finally produce a grid map. To this end, we construct a Pseudo Dense Scan (PDS) which is an odometry based temporal accumulation of the visual sonar readings emulating omni-directional sensing in order to overcome the sparseness of the visual sonar. Moreover, in order to obtain a much more refined map, we further correct the slight trajectory error incurred in the PDS construction step using Sequential Quadratic Programming (SQP) which is a well-known optimization scheme. Experimental results show that our method can obtain an accurate grid map using a single camera without the need for a high price range sensors or stereo camera.

Autonomous Exploration with Exact Inverse Sensor Models

This paper is focused on probabilistic occupancy grid mapping and motion planning such that a robot may build a map and explore a target area autonomously in real time. The desired path of the robot is developed in an optimal fashion to maximize the information gain from the sensor measurements on its path, thereby increasing the accuracy and efficiency of mapping, while explicitly considering the sensor limitations such as the maximum sensing range and viewing angle. Most current exploration techniques require frequent human intervention, often developed for omnidirectional sensors with infinite range. The proposed research is based on realistic assumptions on sensor capabilities. The unique contribution is that the mapping and autonomous exploration techniques are systematically developed in a rigorous, probabilistic formulation. The mapping approach exploits the probabilistic properties of the sensor and map explicitly, and the autonomous exploration is designed to maximize the expected map information gain, thereby improving the efficiency of the mapping procedure and the quality of the map substantially. The efficacy of the proposed optimal approach is illustrated by both numerical simulations and experimental results.