Query: slam path planning

title: On line 3D active pose graph SLAM based on key poses using graph topology and sub maps
abstract: In this paper, we present an on line active pose graph simultaneous localization and mapping (SLAM) frame work for robots in three dimensional (3D) environments using graph topology and sub maps. This framework aims to find the best trajectory for loop closure by re visiting old poses based on the T optimality and D optimality metrics of the Fisher information matrix (FIM) in pose graph SLAM. In order to reduce computational complexity, graph topologies are introduced, including weighted node degree (T optimality metric) and weighted tree connectivity (D optimality metric) to choose a candidate trajectory and several key poses. With the help of the key poses, a sampling based path planning method and a continuous time trajectory optimization method are combined hierarchically and applied in the whole framework. So as to further improve the real time capability of the method, the sub map joining method is used in the estimation and planning process for large scale active SLAM problems. In simulations and experiments, we validate our approach by comparing against existing methods, and we demonstrate the on line planning part using a quad rotor unmanned aerial vehicle (UAV)

title: TOWARDS CONTINUOUS CONTROL FOR MOBILE ROBOT NAVIGATION: A REINFORCEMENT LEARNING AND SLAM BASED APPROACH
abstract: We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired target location based on deep deterministic policy gradient (DDPG) Additionally, the algorithm is enhanced by making use of the available knowledge of the environment provided by a grid based SLAM with Rao Blackwellized particle filter algorithm in order to shape the reward function in an attempt to improve the convergence rate, escape local optima and reduce the number of collisions with the obstacles. A comparison is made between a reward function shaped based on the map provided by the SLAM algorithm and a reward function when no knowledge of the map is available. Results show that the required learning time has been decreased in terms of number of episodes required to converge, which is 560 episodes compared to 1450 episodes in the standard RL algorithm, after adopting the proposed approach and the number of obstacle collision is reduced as well with a success ratio of 83% compared to 56% in the standard RL algorithm. The results are validated in a simulated experiment on a skid steering mobile robot.

title: Autonomous planetary rover navigation via active SLAM
abstract: This paper aims to introduce an efficient active simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.  A three dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.  The proposed approach is able to extract distinguishable 3D landmarks for feature re visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.  This paper proposes a novel active SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.

title: Relative Pose Estimation of Visual SLAM Based on Convolutional Neural Networks
abstract: Pose estimation is the core module of the visual SLAM system, which is usually calculated by the front end visual odometry. The calculated pose gives the initial estimation of the camera pose that is the basis for later map construction and path planning. The target of pose estimation is to calculate the relative motion between two frames. The absolute pose of the latter frame can be calculated according to the absolute pose of the previous frame and the relative transformation matrix of the two frames. The main methods for calculating the relative motion of two frames are feature based method and direct method, and both of them have been successfully applied in various visual SLAM systems. But they all have the disadvantage of needing to carefully adjust the parameters, and sensitive to environment and illumination changes, image blur and other factors. With the great success of deep learning in the field of computer vision, researchers began to explore the use of deep learning methods to solve pose estimation problems. This paper proposes a neural network model for calculating the relative motion of two frames. The training and testing are carried out on a public dataset. The experimental results show that the model shows good performance for known scenes and has certain generalization ability for unknown environments, proving that deep learning methods have the potential to solve the problem of pose estimation.

title: Development of an efficient laser grid mapping technique: P SLAM
abstract: Occupancy grid mapping in the field of mobile robotics is an integrative and promising solution for the various prerequisite such as path planning, autonomous navigation, localization, SLAM (Simultaneously Localization And Mapping) etc. The reliability of the occupancy grid mapping depends upon diverse parameters such as sensor accuracy, size of a grid cell, intrinsic and extrinsic parameters, sensor registration, sensor modelling, scanning angle, ambient conditions, etc. This research reveals the uncertainty in the generation of the laser occupancy grid map with the implementation of conventional laser geometry technique when the detected obstacle/target is on the perimeter of the grid cell. During autonomous navigation, the obstacle and the mobile robot are in a dynamic state that consequence in the wrong perception of the environment by identifying the wrong grid cell as occupied in the occupancy grid map when the obstacle is on the perimeter of the grid cell. The examined error is reduced by a newly designed Perimeter based SLAM (P SLAM) technique based on the vector algebra, laser geometry, Inverse Sensor Model and coordinate system. The obtained results with the implementation of P SLAM are validated w.r.t conventional approaches with qualitative and quantitative analysis by performing real world experiments.

title: Evaluating SLAM in an urban dynamic environment
abstract: Simultaneous Localization And Mapping (SLAM) algorithms provide accurate localization for autonomous vehicles and provide essential information for the path planning module. However, SLAM algorithms as sume a static environment in order to estimate a location. This assumption influences the pose estimation in dynamic urban environments. The impact of this assumption on day to day scenarios of an intelligent vehicle is unknown. A deeper understanding on the effect of dynamic scenarios in an urban environment could lead to simple and robust solutions for SLAM algorithms in intelligent vehicles. The objective of this research is to develop a methodology that isolates the effect of an urban dynamic environment on the per formance of a SLAM algorithm. This requires constant environment conditions including constant weather conditions, lighting conditions and identical trajectories over time. The methodology is tested with a stereo feature based V SLAM algorithm called ORB SLAM [19] which illustrates the in depth analysis that is possi ble with this experiment. The main research question is: How does a dynamic urban environment influence the pose estimation accuracy of stereo ORB SLAM? Two specific dynamic scenarios are designed to represent a dynamic urban environment: driving behind another vehicle and vehicles approaching on the other side of the road. On these scenarios, an in depth anal ysis of ORB SLAM is performed to observe how the algorithm's design influences the robustness to a dynamic environment. Functions within the algorithm are bypassed to analyze the effect on the performance. Specifi cally, the place recognition function and map point filtering function are bypassed. The analysis proofs which functions assist in the overall robustness to a dynamic environment. Moreover, an analysis is performed of the algorithm in localization mode to research the effect of utilizing maps that were created under different conditions. The knowledge gained from the full analysis can be utilized to improve other V SLAM algorithms. The experiment is performed in CARLA [6] an open source simulator. CARLA provides an elaborate sen sor suite which support multiple camera setups and LIDAR sensors. Furthermore, the simulator provides free maps which represent realistic urban environments and allows for easy and accurate access to the ground truth position. A setup is designed with the simulator that allows complete isolation of the effect of a dy namic environment. The setup allows full control of lighting conditions, weather conditions and allows iden tical trajectories over time in different dynamic scenarios. Each scenario is simulated over several different trajectories in which the camera images are converted to rosbags. Each variation of the ORB SLAM algorithm is tested on the produced rosbags. The resulting pose estimations in dynamic conditions are compared to the pose estimations made during static conditions to analyze the effect of dynamic scenarios on the perfor mance of the algorithm. The method successfully isolated the effect of a dynamic environment on the performance of stereo ORB SLAM. It allows for a detailed analysis which aids in finding the source of performance differences. In general, stereo ORB SLAM displays robust behavior to a dynamic environment. The experiment shows that the algo rithm is sensitive to false relocalization when the stereo camera setup is driving 10 meters behind another vehicle for a long period of time. During these conditions, ORB SLAM cannot provide accurate pose esti mations even when the place recognition module is deactivated. Furthermore, the map point filtering does increase the robustness in certain dynamic scenarios. Finally, the data suggests that utilizing maps created in different conditions does influence the pose estimation in localization mode. However, more data is needed to confirm these results. The methodology has proven its value for in depth analysis of robustness to an urban dynamic environ ment for a SLAM algorithm. This experiment is not limited to ORB SLAM but could be utilized for other monocular and stereo V SLAM methods, as well as LIDAR based methods. New solutions can be developed to increase robustness to a dynamic environment and tested on the same rosbags. This methodology could be an important tool for the development SLAM algorithms for intelligent vehicles.

title: An Approach for Representing Maps in SLAM Based on Grid Maps and Sparse Matrices
abstract: Graphs and matrices are widely used to represent maps in solutions for Simultaneous Localization and Mapping (SLAM) Space representation is crucial for map creation, finding the shortest path, planning a trajectory or route, tracking landmarks, etc. The choice of the data structure can present disadvantages such as more memory consumption when using matrices, or, the need of a transforming function to represent a map when graphs (as structure) are applied as structure. This paper propose a representing map using a storing structure that allows dynamic growth and optimization of memory consumption in SLAM. The so called MPTE SLAM structure, proposed here, suggests the usage of a sparse matrix of occupancy matrices to represent a map for SLAM. The goal of this approach is to reduce the use of memory (if compared with traditional metric maps also) also having a compact representation suitable for path planning and other tasks for autonomous robots; without the need of a transforming function (if compared with graph based solutions that return a map) This approach allows the addition and updating of values of the sub matrices quickly and with dynamic growth on the overall map within the structure in the same way graphs permit; also it is suitable for a real time implementation of solutions for SLAM. For implementation and testing, a non holonomic mobile robot in an indoor environment was used. The final results showed that the MPTE SLAM structure uses less memory than metric maps when optimal sub matrices sizes are applied. Through experimentation an optimal size for submatrices (10x10) was determined; this size yielded a memory consumption less than 4.3 GB in all test experiments; also, it was observed that this approach also has dynamic growth in the structure plus the no loss of its metric nature which gives it an advantage over graph based representations.

title: Autonomous navigation in unknown environments using robust SLAM
abstract: Autonomous navigation in unknown environment is a big challenge. In this paper, we combine the SLAM (simultaneous localization and mapping) with the path planning method. We first modify the classical SLAM with sliding mode technique, such that it is robust in the unknown environment. Then we analyze the algorithm using the "known space" and "free space" conditions, and propose the polar histogram path planning based on these conditions. We use Monte Carlo method to evaluate the performance of our algorithms. Simulation results show that our autonomous navigation algorithms are better than the others in unknown environment.

title: Research on the SLAM of Mobile Robot Based on Particle Filter Method
abstract: This paper mainly studies mobile robots under ROS (Robot Operating System) combined with lidar and odometer information collection, to solve the indoor robot SLAM (Simul taneous Localization and Mapping) and path planning technology problems. For this problem, this paper solves this problem based on ROS and mobile robot. In order to improve the computational efficiency and positioning accuracy of the robot in SLAM, SALM algorithm based on particle filter is adopted in this paper to complete the positioning and map creation functions of the robot in the unknown environment. At the same time, the path planning algorithm provided by ROS navigation function package is used to complete the navigation of the robot in the global map of the known environment, and the obstacle avoidance function of local map. The experimental results show that this technology can construct high precision two dimensional grid map and realize high precision positioning and optimal path planning of the robot.

title: Long Horizon Active SLAM system for multi agent coordinated exploration
abstract: Exploring efficiently an unknown environment with several autonomous agents is a challenging task. In this work we propose an multi agent Active SLAM method that is able to evaluate a long planning horizon of actions and perform exploration while maintaining estimation uncertainties bounded. Candidate actions are generated using a variant of the Rapidly exploring Random Tree approach (RRT* followed by a joint entropy minimization to select a path. Entropy estimation is performed in two stages, a short horizon evaluation is carried using exhaustive filter updates while entropy in long horizons is approximated considering reductions on predicted loop closures between robot trajectories. We pursue a fully decentralized exploration approach to cope with typical uncertainties in multiagent coordination. We performed simulations for decentralized exploration planning, which is both dynamically adapting to new situations as well as concerning long horizon plans.