Abstract: In this paper we present an extension to the KinectFusion algorithm that permits dense mesh-based mapping of extended scale environments in real-time. This is achieved through (i) altering the original algorithm such that the region of space being mapped by the KinectFusion algorithm can vary dynamically, (ii) extracting a dense point cloud from the regions that leave the KinectFusion volume due to this variation, and, (iii) incrementally adding the resulting points to a triangular mesh representation of the environment. The system is implemented as a set of hierarchical multi-threaded components which are capable of operating in real-time. The architecture facilitates the creation and integration of new modules with minimal impact on the performance on the dense volume tracking and surface reconstruction modules. We provide experimental results demonstrating the system’s ability to map areas considerably beyond the scale of the original KinectFusion algorithm including a two story apartment and an extended sequence taken from a car at night. In order to overcome failure of the iterative closest point (ICP) based odometry in areas of low geometric features we have evaluated the Fast Odometry from Vision (FOVIS) system as an alternative. We provide a comparison between the two approaches where we show a trade off between the reduced drift of the visual odometry approach and the higher local mesh quality of the ICP-based approach. Finally we present ongoing work on incorporating full simultaneous localisation and mapping (SLAM) pose-graph optimisation.

Abstract: In this paper, we describe a system that enables a low-cost quadrocopter coupled with a ground-based laptop to navigate autonomously in previously unknown and GPS-denied environments. Our system consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion and state estimation and a PID controller to generate steering commands. Next to a working system, the main contribution of this paper is a novel, closed-form solution to estimate the absolute scale of the generated visual map from inertial and altitude measurements. In an extensive set of experiments, we demonstrate that our system is able to navigate in previously unknown environments at absolute scale without requiring artificial markers or external sensors. Furthermore, we show (1) its robustness to temporary loss of visual tracking and significant delays in the communication process, (2) the elimination of odometry drift as a result of the visual SLAM system and (3) accurate, scale-aware pose estimation and navigation.

Abstract: Indoor pedestrian tracking extends location-based services to indoor environments. Typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of accuracy and coverage, they require an indoor map, which is typically not available to the average user and involves significant training costs. A practical indoor pedestrian tracking approach should consider the indoor environment without a pretrained database or floor plan. In this paper, we present an indoor pedestrian tracking system, called SmartSLAM, which automatically constructs an indoor floor plan and radio fingerprint map for anonymous buildings using a smartphone. The scheme employs odometry tracing using inertial sensors, an observation model using Wi-Fi signals, and a Bayesian estimation for floor-plan construction. SmartSLAM is a true simultaneous localization and mapping implementation that does not necessitate additional devices, such as laser rangefinders or wheel encoders. We implemented the scheme on off-the-shelf smartphones and evaluated the performance in our university buildings. Despite inherent tracking errors from noisy sensors, SmartSLAM successfully constructed indoor floor plans.

Abstract: A global localization system combining odometry data with radio frequency identification (RFID) readings is proposed. RFID tags are placed at the ceiling of the environment and can be detected by a mobile robot unit traveling below them. The detection of the tags is the only information used in the proposed approach (no distance or bearing to the tag is considered available), but differently from similar localization setups reported in the literature, only a small number (about one each square meter or less) of tags are used. This is possible using a suitable tag's antenna in ultrahigh frequency band, expressly designed to obtain regular and stable RFID detection regions, which allows us to consider an efficient Kalman filtering approach to fuse RFID readings with the vehicle odometry data. A satisfactory performance is achieved, with an average position error of about 0.1 m. The hardware/software localization setup described in this paper is cheap and easy to use and may provide a satisfactory approach in several industrial and domestic scenarios.

Abstract: Reliable knowledge of the ego position for vehicles is a crucial requirement for many automotive applications. In order to solve this problem for satellite-based localization in dense urban areas, multipath situations need to be handled carefully. This paper proposes a lightweight multipath detection algorithm which is based on dynamically built 3D environmental maps. The algorithm is evaluated with simulated and real-world data. Furthermore, it is applied to a combined GPS and GLONASS system in combination with a loosely coupled integration of odometry measurements from the vehicle.

Abstract: This paper presents a mobile robot localization system for an indoor environment using an inexpensive sensor system. The extended Kalman filter (EKF) and the particle filter (PF) is used for sensor fusion in pose estimation in order to minimize uncertainty in robot localization. The robot is maneuvered in a known environment with some visual landmarks. The prediction phase of the EKF and the PF are implemented using the information from the robot odometry whose error may accumulate over time. The update phase uses the Kinect measurements of the landmarks to correct the robot's pose. Experiment results show that, despite its low cost, the accuracy of the localization is comparable with most state-of-the-art odometry based methods.

Abstract: This paper presents two new, efficient solutions to the two-view, relative pose problem from three image point correspondences and one common reference direction. This three-plus-one problem can be used either as a substitute for the classic five-point algorithm, using a vanishing point for the reference direction, or to make use of an inertial measurement unit commonly available on robots and mobile devices where the gravity vector becomes the reference direction. We provide a simple, closed-form solution and a solution based on algebraic geometry which offers numerical advantages. In addition, we introduce a new method for computing visual odometry with RANSAC and four point correspondences per hypothesis. In a set of real experiments, we demonstrate the power of our approach by comparing it to the five-point method in a hypothesize-and-test visual odometry setting.

Abstract: We present a real-time system that enables a highly capable dynamic quadruped robot to maintain an accurate 6-DOF pose estimate (better than 0.5m over every 50m traveled) over long distances traversed through complex, dynamic outdoor terrain, during day and night, in the presence of camera occlusion and saturation, and occasional large external disturbances, such as slips or falls. The system fuses a stereo-camera sensor, inertial measurement units (IMU), and leg odometry with an Extended Kalman Filter (EKF) to ensure robust, low-latency performance. Extensive experimental results obtained from multiple field tests are presented to illustrate the performance and robustness of the system over hours of continuous runs over hundreds of meters of distance traveled in a wide variety of terrains and conditions.

Abstract: This paper describes extensions to the Kintinuous [1] algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D visual odometry algorithm; (iii) advanced fused realtime surface coloring. These extensions are validated with extensive experimental results, both quantitative and qualitative, demonstrating the ability to build dense fully colored models of spatially extended environments for robotics and virtual reality applications while remaining robust against scenes with challenging sets of geometric and visual features.

Abstract: This paper focuses on fast 3D point cloud registration in cluttered urban environments. There are three main steps in the pipeline: Firstly a fast region growing planar segmentation algorithm is employed to extract the planar surfaces. Then the area of each planar patch is calculated using the image-like structure of organized point cloud. In the last step, the registration is defined as a correlation problem, a novel search algorithm which combines heuristic search with pruning using geometry consistency is utilized to find the global optimal solution in a subset of SO(3) ∪ R3, and the transformation is refined using weighted least squares after finding the solution. Since all possible transformations are traversed, no prior pose estimation from other sensors such as odometry or IMU is needed, makeing it robust and can deal with big rotations.

Abstract: In this paper, we present a novel approach to planetary rover localization that incorporates sun sensor and inclinometer data directly into a stereo visual odometry pipeline. Utilizing the absolute orientation information provided by the sun sensor and inclinometer significantly reduces the error growth of the visual odometry path estimate. The measurements have very low computation, power, and mass requirements, providing localization improvement at nearly negligible cost. We describe the mathematical formulation of error terms for the stereo camera, sun sensor, and inclinometer measurements, as well as the bundle adjustment framework for determining the maximum likelihood vehicle transformation. Extensive results are presented from experimental trials utilizing data collected during a 10-km traversal of a Mars analogue site on Devon Island in the Canadian high Arctic. We also illustrate how our approach can be used to reduce the computational burden of visual odometry for planetary exploration missions. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Abstract: Long term autonomy in robots requires the ability to reconsider previously taken decisions when new evidence becomes available Loop closing links generated by a place recognition system may become inconsistent as additional evidence arrives This paper is concerned with the detection and exclusion of such contradictory information from the map being built, in order to recover the correct map estimate We propose a novel consistency based method to extract the loop closure regions that agree both among themselves and with the robot trajectory over time We also assume that the contradictory loop closures are inconsistent among themselves and with the robot trajectory We support our proposal, the RRR algorithm, on well-known odometry systems, eg visual or laser, using the very efficient graph optimization framework g2o as back-end We back our claims with several experiments carried out on real data

Abstract: The method for localisation and mapping of pedestrians or robots using Wireless Access Points comprises the following steps: Wireless Signal Strength and/or time delay measurements from wireless access points (e.g. Wireless Local Area Network access points (WLAN, Wifi, WIMAX), or mobile radio base stations (e.g. GSM, UMTS, LTE, 4G, IS95 or RFID tags or transmitters) are taken at regular or irregular time instances by a device carried by the pedestrian or robot in addition to odometry measurements (e.g. human step measurements, human pedestrian dead-reckoning, robot or wheelchair wheel counter measurements, robot motor or wheelchair motor control inputs), and providing a particle filter which has a state model that comprises the pedestrian or robot location history for each particle, and also the location probability distribution of one or more wireless access points, wherein at each time-step of the particle filter each particle of the particle filter is weighted and/or propagated according to the odometry measurements and weighted and/or propagated according to the wireless measurement, wherein at each time-step of the particle filter the location probability distribution of the wireless access points for each particle is updated according to the measurement and the previous location probability distribution of that particle, and wherein the location of the pedestrian or robot and/or the map of the wireless access point(s) is extracted from the particle population (e.g. from the state of the particle with greatest weight, from the weighted state across all particles, from the state of a randomly chosen particle, from the state of the maximum likelihood particle).

Abstract: Simultaneous Localization and Mapping (SLAM) algorithms with multiple autonomous robots have received considerable attention in recent years. In general, SLAM algorithms use odometry information and measurements from exteroceptive sensors of robots. The accuracy of these measurements and the performance of the corresponding SLAM algorithm directly affect the overall success of the system. This paper presents comparative performance evaluations of three Simultaneous Localization and Mapping (SLAM) algorithms using Extended Kalman Filter (EKF), Compressed Extended Kalman Filter (CEKF) and Unscented Kalman Filter (UKF). Specifically, it focuses on their SLAM performances and processing time requirements. To show the effect of CPU power on the processing time of SLAM algorithms, two notebooks and a netbook with different specifications have been used. Comparative simulation results show that processing time requirements are consistent with the computational complexities of SLAM algorithms. The results we obtained are consistent with the CPU power tests of independent organizations and show that higher processing power decreases processing time accordingly. The results also show that CEKF is more suitable for outdoor SLAM applications where there are a lot of natural and artificial features.

Abstract: In this paper we present a resource-adaptive framework for real-time vision-aided inertial navigation. Specifically, we focus on the problem of visual-inertial odometry (VIO), in which the objective is to track the motion of a mobile platform in an unknown environment. Our primary interest is navigation using miniature devices with limited computational resources, similar for example to a mobile phone. Our proposed estimation framework consists of two main components: (i) a hybrid EKF estimator that integrates two algorithms with complementary computational characteristics, namely a sliding-window EKF and EKF-based SLAM, and (ii) an adaptive image-processing module that adjusts the number of detected image features based on the availability of resources. By combining the hybrid EKF estimator, which optimally utilizes the feature measurements, with the adaptive image-processing algorithm, the proposed estimation architecture fully utilizes the system's computational resources. We present experimental results showing that the proposed estimation framework is capable of real-time processing of image and inertial data on the processor of a mobile phone.

Abstract: In this paper, we present a novel computer vision framework for precise localization of a mobile robot on sidewalks. In our framework, we combine stereo camera images, visual odometry, satellite map matching, and a sidewalk probability transfer function obtained from street maps in order to attain globally corrected localization results. The framework is capable of precisely localizing a mobile robot platform that navigates on sidewalks, without the use of traditional wheel odometry, GPS or INS inputs. On a complex 570-meter sidewalk route, we show that we obtain superior localization results compared to visual odometry and GPS.

Abstract: In this paper, we present a method for the recovery of position and absolute attitude (including pitch, roll and yaw) using a novel fusion of monocular visual odometry and GPS measurements in a similar manner to a classic loosely coupled GPS/INS error state navigation filter. The proposed filter does not require additional restrictions or assumptions such as platform-specific dynamics, map matching, feature tracking, visual loop closing, gravity vector or additional sensors such as an inertial measurement unit or magnetic compass. An observability analysis of the proposed filter is performed, showing that the scale factor, position and attitude errors are fully observable under acceleration that is non-parallel to the velocity vector in the navigation frame. The observability properties of the proposed filter are demonstrated using numerical simulations. We conclude the article with an implementation of the proposed filter using real flight data collected from a Cessna 172 equipped with a downwards-looking camera and GPS, showing the feasibility of the algorithm in real-world conditions.

Abstract: System state estimation is an essential part for robot navigation and control. A combination of Inertial Navigation Systems (INS) and further exteroceptive sensors such as cameras or laser scanners is widely used. On small robotic systems with limitations in payload, power consumption and computational resources the processing of exteroceptive sensor data often introduces time delays which have to be considered in the sensor data fusion process. These time delays are especially critical in the estimation of system velocity. In this paper we present a state estimation framework fusing an INS with time delayed, relative exteroceptive sensor measurements. We evaluate its performance for a highly dynamic flight system trajectory including a flip. The evolution of velocity and position errors for varying measurement frequencies from 15Hz to 1Hz and time delays up to 1s is shown in Monte Carlo simulations. The filter algorithm with key frame based odometry permits an optimal, local drift free navigation while still being computationally tractable on small onboard computers. Finally, we present the results of the algorithm applied to a real quadrotor by flying from inside a house out through the window.

Abstract: We present a visual odometry system for indoor navigation with a focus on long-term robustness and consistency. As our work is targeting mobile phones, we employ monocular SLAM to jointly estimate a local map and the device's trajectory. We specifically address the problem of estimating the scale factor of both, the map and the trajectory. State-of-the-art solutions approach this problem with an Extended Kalman Filter (EKF), which estimates the scale by fusing inertial and visual data, but strongly relies on good initialization and takes time to converge. Each visual tracking failure introduces a new arbitrary scale factor, forcing the filter to re-converge. We propose a fast and robust method for scale initialization that exploits basic geometric properties of the learned local map. Using random projections, we efficiently compute geometric properties from the feature point cloud produced by the visual SLAM system. From these properties (e.g., corridor width or height) we estimate scale changes caused by tracking failures and update the EKF accordingly. As a result, previously achieved convergence is preserved despite re-initializations of the map. To minimize the time required to continue tracking after failure, we perform recovery and re-initialization in parallel. This increases the time available for recovery and hence the likelihood for success, thus allowing almost seamless tracking. Moreover, fewer re-initializations are necessary. We evaluate our approach using extensive and diverse indoor datasets. Results demonstrate that errors and convergence times for scale estimation are considerably reduced, thus ensuring consistent and accurate scale estimation. This enables long-term odometry despite of tracking failures which are inevitable in realistic scenarios.

Abstract: In this paper, we present a robust framework suitable for conducting three-dimensional simultaneous localization and mapping (3D SLAM) in a planetary work site environment. Operation in a planetary environment imposes sensing restrictions, as well as challenges due to the rugged terrain. Utilizing a laser rangefinder mounted on a rover platform, we have demonstrated an approach that is able to create globally consistent maps of natural, unstructured 3D terrain. The framework presented in this paper utilizes a sparse-feature-based approach and conducts data association using a combination of feature constellations and dense data. Because of feature scarcity, odometry measurements are also incorporated to provide additional information in feature-poor regions. To maintain global consistency, these measurements are resolved using a batch alignment algorithm, which is reinforced with heterogeneous outlier rejection to improve its robustness to outliers in either measurement type (i.e., laser or odometry). Finally, a map is created from the alignment estimates and the dense data. Extensive validation of the framework is provided using data gathered at two different planetary analogue facilities, which consist of 50 and 102 3D scans, respectively. At these sites, root-mean-squared mapping errors of 4.3 and 8.9 cm were achieved. Relative metrics are utilized for localization accuracy and map quality, which facilitate detailed analysis of the performance, including failure modes and possible future improvements. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Abstract: In modern railway Automatic Train Protection and Automatic Train Control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. Simplified two-dimensional models of railway vehicles have been usually used for Hardware in the Loop test rig testing of conventional odometry algorithms and of on-board safety relevant subsystems (like the Wheel Slide Protection braking system) in which the train speed is estimated from the measures of the wheel angular speed. Two-dimensional models are not suitable to develop solutions like the inertial type localisation algorithms (using 3D accelerometers and 3D gyroscopes) and the introduction of Global Positioning System (or similar) or the magnetometer. In order to test these algorithms correctly and increase odometry performances, a three-dimensional multibody model of a railway vehicle has been developed, using Matlab-Simulink™, including an efficient contact model which can simulate degraded adhesion conditions (the development and prototyping of odometry algorithms involve the simulation of realistic environmental conditions). In this paper, the authors show how a 3D railway vehicle model, able to simulate the complex interactions arising between different on-board subsystems, can be useful to evaluate the odometry algorithm and safety relevant to on-board subsystem performances.

Abstract: This paper proposes a GPU (graphics processing unit)-based real-time RGB-D (red-green-blue depth) 3D SLAM (simultaneous localization and mapping) system. RGB-D data contain 2D image and per-pixel depth information. First, 6-DOF (degree-of-freedom) visual odometry is obtained through the 3D-RANSAC (three-dimensional random sample consensus) algorithm with image features. And a projective ICP (iterative closest point) algorithm gives an accurate odometry estimation result with depth information. For speed up extraction of features and ICP computation, GPU-based parallel computation is performed. After detecting loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and 3D map.

Abstract: A practical localization technology for underground drift networks—such as those excavated in the practice of underground mining—has yet to become commercially available. This paper focuses on the problem of mapping GPS-deprived underground environments with the eventual goal of using these maps for navigation. Recent industry-directed work in the creation of a landmark-bounded occupancy grid mapping tool that combines odometry, scanning laser data, and sporadically placed passive RFID tags is described. Unlike other work, the suggested approach holds the philosophy that precise localization of the actual landmark locations is not necessary; rather, landmarks serve as a global means for partitioning the map. Successful field experiments were conducted in two underground environments, with the results used to conduct a basic analysis of the described method. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Abstract: Localization plays a significant role in the autonomous navigation of a mobile robot. This paper investigates mobile robot localization based on Extended Kalman Filter(EKF) algorithm and a feature based map. Corner angles in the environment are detected as the features, and the detailed processes of feature extraction are described. Then the motion model and odometry information are elaborated, and the EKF localization algorithm is presented. Finally, the experimental result is given to verify the feasibility and performance of the proposed localization algorithm.

Abstract: This paper addresses the problem of tracking a group of mobile sensors in an environment where there is intermittent or no access to a localization service, such as the Global Positioning System. Example applications include tracking personnel underground or animals under dense tree canopies. We assume that each sensor uses inertial, visual or mechanical odometry to measure its relative movement as a series of displacement vectors. Each displacement vector suffers a small quantity of error which compounds, causing the overall accuracy of the positional estimate to decrease with time. The primary contribution of this paper is a novel offline method of counteracting this error by exploiting opportunistic radio encounters between sensors. We fuse encounter information with the displacement vectors to build a graph that models sensor mobility. We show that two dimensional sensor tracking is equivalent to finding an embedding of this graph in the plane. Finally, using radio, inertial and ground truth trace data, we conduct simulations to observe how the number of anchors, transmission range and radio noise affect the performance of the proposed model. We compare these results to those from a competing model in the literature.

Abstract: Visual homing has been widely studied in the past decade. It enables a mobile robot to move to a Home position using only information extracted from visual data. However, integration of homing algorithms into real applications is not widely studied and poses a number of significant challenges. Failures often occur due to moving people within the scene and variations in illumination. We present a novel integrated indoor topological navigation framework, which combines odometry motion with visual homing algorithms. We show robustness to scene variation and real-time performance through a series of tests conducted in four real apartments and several typical indoor scenes, including doorways, offices etc.

Abstract: When GPS, or other absolute positioning, is un-available, terrain-relative velocity is crucial for dead reckoning and the vehicle's pose estimate. Unfortunately, the position-denied accuracy of the inertial navigation system (INS) is governed by the performance of the linear velocity aiding sources, such as wheel odometry, which are typically corrupted by large systematic errors due to wheel slip. As a result, affordable terrestrial inertial navigation is ineffective in estimating position when denied position fixes for an extended period of time. For mobile robots, the mapping between inputs and resultant behavior depends critically on terrain conditions which vary significantly over time and space which cannot be pre-programmed. Past work has used Integrated Perturbative Dynamics (IPD) to identify successively systematic and stochastic models of wheel slip, but treated the pose filter only as input without improving the odometry measurements used for vehicle navigation. We present a unique approach of a predictive vehicle slip model in a delayed state extended Kalman filter. The relative pose difference between the current state and delayed state is used as a measurement update to the vehicle slip model. These results create an opportunity to compensate for wheel slip effects in terrestrial inertial navigation. This paper presents the design, calibration, and verification of such a system and concludes that the position-denied performance of the compensated system is far superior.

Abstract: We propose a framework for utilizing fixed ultra-wideband ranging radio nodes to track a moving target radio node in an environment without guaranteed line of sight or accurate odometry. For the case where the fixed nodes' locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. For the case of unknown fixed node locations, we present a two-step approach that first reconstructs the target node's path using Gaussian Process Latent Variable models (GPLVMs) and then uses that path to determine the locations of the fixed nodes. We present experiments verifying our algorithm in an office environment, and we compare our results to those generated by online and batch SLAM methods, as well as odometry mapping. Our algorithm is successful at tracking a moving target node without odometry and mapping the locations of fixed nodes using radio ranging data that are both noisy and intermittent.

Abstract: For service robotics, localization is an essential component required in many applications, e.g. indoor robot navigation. Today, accurate localization relies mostly on high-end devices, such as A.R.T. DTrack, VICON systems or laser scanners. These systems are often expensive and, thus, require substantial investments. In this paper, our focus is on the development of a localization method using low-priced devices, such as cameras, while being sufficiently accurate in tracking performance. Vision data contains much information and potentially yields high tracking accuracy. However, due to high computational requirements vision-based localization can only be performed at a low frequency. In order to speed up the visual localization and increase accuracy, we combine vision information with robots odometry using a Kalman-Filter. The resulting approach enables sufficiently accurate tracking performance (errors in the range of few cm) at a frequency of about 35Hz. To evaluate the proposed method, we compare our tracking performance with the high precision A.R.T. DTrack localization as ground truth. The evaluations on real robot show that our low-priced localization approach is competitive for indoor robot localization tasks.