Simultaneous Localization and Mapping (SLAM) on NAO

Simultaneous Localization and Mapping (SLAM) is a navigation and mapping method used by autonomous robots and moving vehicles. SLAM is mainly concerned with the problem of building a map in an unknown environment and concurrently navigating through the environment using the map. Localization is of utmost importance to allow the robot to keep track of its position with respect to the environment and the common use of odometry proves to be unreliable. SLAM has been proposed as a solution by previous research to provide more accurate localization and mapping on robots. This project involves the implementation of the SLAM algorithm in the humanoid robot NAO by Aldebaran Robotics. The SLAM technique will be implemented using vision from the single camera attached to the robot to map and localize the position of NAO in the environment. The result details the attempt to implement specifically the chosen algorithm, 1-Point RANSAC Inverse Depth EKF Monocular SLAM by Dr Javier Civera on the robot NAO. The algorithm is shown to perform well for smooth motions but on the humanoid NAO, the sudden changes in motion produces undesirable results.This study on SLAM will be useful as this technique can be widely used to allow mobile robots to map and navigate in areas which are deemed unsafe for humans

A Real-Time Robust SLAM for Large-Scale Outdoor Environments

International audienceThe problem of simultaneous localization and mapping (SLAM) is still a challenging issue in large-scale unstructured dynamic environments. In this paper, we introduce a real-time reliable SLAM solution with the capability of closing the loop using exclusive laser data. In our algorithm, a universal motion model is presented for initial pose estimation. To further refine robot pose, we propose a novel progressive refining strategy using a pyramid grid-map based on Maximum Likelihood mapping framework. We demonstrate the success of our algorithm in experimental result by building a consistent map along a 1.2 km loop trajectory (an area about 100,000 m2) in an increasingly unstructured outdoor environment, with people and other clutter in real time

Improving Occupancy Grid FastSLAM by Integrating Navigation Sensors

When an autonomous vehicle operates in an unknown environment, it must remember the locations of environmental objects and use those object to maintain an accurate location of itself. This vehicle is faced with Simultaneous Localization and Mapping (SLAM), a circularly defined robotics problem of map building with no prior knowledge. The SLAM problem is a difficult but critical component of autonomous vehicle exploration with applications to search and rescue missions. This paper presents the first SLAM solution combining stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The FastSLAM algorithm, modified to make use of the MINS path, observes and maps the environment with a LIDAR unit. The MINS FastSLAM algorithm closes a 140 meter loop with a path error that remains within 1 meter of surveyed truth. This path reduces the error 79% from an odometry FastSLAM output and uses 30% of the particles

Simultaneous Localization and Mapping (SLAM) on NAO

Simultaneous Localization and Mapping (SLAM) is a navigation and mapping method used by autonomous robots and moving vehicles. SLAM is mainly concerned with the problem of building a map in an unknown environment and concurrently navigating through the environment using the map. Localization is of utmost importance to allow the robot to keep track of its position with respect to the environment and the common use of odometry proves to be unreliable. SLAM has been proposed as a solution by previous research to provide more accurate localization and mapping on robots. This project involves the implementation of the SLAM algorithm in the humanoid robot NAO by Aldebaran Robotics. The SLAM technique will be implemented using vision from the single camera attached to the robot to map and localize the position of NAO in the environment. The result details the attempt to implement specifically the chosen algorithm, 1-Point RANSAC Inverse Depth EKF Monocular SLAM by Dr Javier Civera on the robot NAO. The algorithm is shown to perform well for smooth motions but on the humanoid NAO, the sudden changes in motion produces undesirable results.This study on SLAM will be useful as this technique can be widely used to allow mobile robots to map and navigate in areas which are deemed unsafe for humans

Large-scale monocular SLAM by local bundle adjustment and map joining

This paper first demonstrates an interesting property of bundle adjustment (BA), "scale drift correction". Here "scale drift correction" means that BA can converge to the correct solution (up to a scale) even if the initial values of the camera pose translations and point feature positions are calculated using very different scale factors. This property together with other properties of BA makes it the best approach for monocular Simultaneous Localization and Mapping (SLAM), without considering the computational complexity. This naturally leads to the idea of using local BA and map joining to solve large-scale monocular SLAM problem, which is proposed in this paper. The local maps are built through Scale-Invariant Feature Transform (SIFT) for feature detection and matching, random sample consensus (RANSAC) paradigm at different levels for robust outlier removal, and BA for optimization. To reduce the computational cost of the large-scale map building, the features in each local map are judiciously selected and then the local maps are combined using a recently developed 3D map joining algorithm. The proposed large-scale monocular SLAM algorithm is evaluated using a publicly available dataset with centimeter-level ground truth. ©2010 IEEE

New optimization techniques for point feature and general curve feature based SLAM

University of Technology, Sydney. Faculty of Engineering and Information Technology.This doctoral thesis deals with the feature based Simultaneous Localization and Mapping (SLAM) problem. SLAM as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the robot. In feature based SLAM, the robot relies on its ability to extract useful navigation information from the data returned by its sensors. The robot typically starts at an unknown location without priori knowledge of feature locations. From relative observations of features and relative pose measurements, estimates of entire robot trajectory and feature locations can be derived. Thus, the solution to SLAM problem enables an autonomous vehicle navigates in a unknown environment autonomously. The advantage of eliminating the need for artificial infrastructures or a priori topological knowledge of the environment makes SLAM problem one of the hot research topics in the robotics literature. Solution to the SLAM problem would be of inestimable value in a range of applications such as exploration, surveillance, transportation, mining etc. The critical problems for feature based SLAM implementations are as follows: 1) Because SLAM problems are high dimensional, nonlinear and non-convex, when solving SLAM problems, robust optimization techniques are required. 2) When the environment is complex and unstructured, appropriate parametrization method is required to represent environments with minimum information loss. 3) As robot navigates in the environment, the information acquired by the onboard sensor increases. It is essential to develop computationally tractable SLAM algorithms especially for general curve features. This thesis presents the following contributions to feature based SLAM. First, a convex optimization based approach for point feature SLAM problems is developed. Using the proposed method, a unique solution can be obtained without any initial state estimates. It will be shown that, the unique SDP solution obtained from the proposed method is very close to the true solution to the SLAM problem. Second, a general curve feature based SLAM formulation is presented. Instead of scattered points, in this formulation, the environment is represented by a number of continuous curves. Using the new formulation, all the available information from the sensor is utilized in the optimization process. Third, method for converting curve feature to point feature is presented. Using the conversion method, the curve feature SLAM problem can be transferred to point feature SLAM problem and can be solved by the convex optimization based approach

Building maps of large environments using splines and geometric analysis

Recently, a novel solution to the Simultaneous Localization and Map building (SLAM) problem for complex indoor environments was presented, using a set of splines for describing the geometries detected by a laser range finder mounted on a mobile platform. In this paper, a method for exploiting the geometric information underlying in these maps in the data association process is described. The proposed approach uses graphs of relations between simple features extracted from the environment, and a bit encoded implementation for obtaining a maximum clique that relates observations with previously visited areas. This information is used to update the relative positions of a collage of submaps of limited size

Pragma-Oriented Parallelization of the Direct Sparse Odometry SLAM Algorithm

Monocular 3D reconstruction is a challenging computer vision task that becomes even more stimulating when we aim at real-time performance. One way to obtain 3D reconstruction maps is through the use of Simultaneous Localization and Mapping (SLAM), a recurrent engineering problem, mainly in the area of robotics. It consists of building and updating a consistent map of the unknown environment and, simultaneously, saving the pose of the robot, or the camera, at every given time instant. A variety of algorithms has been proposed to address this problem, namely the Large Scale Direct Monocular SLAM (LSD-SLAM), ORB-SLAM, Direct Sparse Odometry (DSO) or Parallel Tracking and Mapping (PTAM), among others. However, despite the fact that these algorithms provide good results, they are computationally intensive. Hence, in this paper, we propose a modified version of DSO SLAM, which implements code parallelization techniques using OpenMP, an API for introducing parallelism in C, C++ and Fortran programs, that supports multi-platform shared memory multi-processing programming. With this approach we propose multiple directive-based code modifications, in order to make the SLAM algorithm execute considerably faster. The performance of the proposed solution was evaluated on standard datasets and provides speedups above 40% without significant extra parallel programming effort.info:eu-repo/semantics/publishedVersio