Cleaning robot navigation using panoramic views and particle clouds as landmarks

The paper describes a visual method for the navigation of autonomous floor-cleaning robots. The method constructs a topological map with metrical information where place nodes are characterized by panoramic images and by particle clouds representing position estimates. Current image and position estimate of the robot are interrelated to landmark images and position estimates stored in the map nodes through a holistic visual homing method which provides bearing and orientation estimates. Based on these estimates, a position estimate of the robot is updated by a particle filter. The robot’s position estimates are used to guide the robot along parallel, meandering lanes and are also assigned to newly created map nodes which later serve as landmarks. Computer simulations and robot experiments confirm that the robot position estimate obtained by this method is sufficiently accurate to keep the robot on parallel lanes, even in the presence of large random and systematic odometry errors. This ensures an efficient cleaning behavior with almost complete coverage of a rectangular area and only small repeated coverage. Furthermore, the topological-metrical map can be used to completely cover rooms or apartments by multiple meander parts.     

Autonomous robotic exploration using a utility function based on Rényi’s general theory of entropy

In this paper we present a novel information-theoretic utility function for selecting actions in a robot-based autonomous exploration task. The robot’s goal in an autonomous exploration task is to create a complete, high-quality map of an unknown environment as quickly as possible. This implicitly requires the robot to maintain an accurate estimate of its pose as it explores both unknown and previously observed terrain in order to correctly incorporate new information into the map. Our utility function simultaneously considers uncertainty in both the robot pose and the map in a novel way and is computed as the difference between the Shannon and the Rényi entropy of the current distribution over maps. Rényi’s entropy is a family of functions parameterized by a scalar, with Shannon’s entropy being the limit as this scalar approaches unity. We link the value of this scalar parameter to the predicted future uncertainty in the robot’s pose after taking an exploratory action. This effectively decreases the expected information gain of the action, with higher uncertainty in the robot’s pose leading to a smaller expected information gain. Our objective function allows the robot to automatically trade off between exploration and exploitation in a way that does not require manually tuning parameter values, a significant advantage over many competing methods that only use Shannon’s definition of entropy. We use simulated experiments to compare the performance of our proposed utility function to these state-of-the-art utility functions. We show that robots that use our proposed utility function generate maps with less uncertainty and fewer visible artifacts and that the robots have less uncertainty in their pose during exploration. Finally, we demonstrate that a real-world robot using our proposed utility function is able to successfully create a high-quality map of an indoor office environment.     

Appearance-based mapping and localization for mobile robots using a feature stability histogram

The strength of appearance-based mapping models for mobile robots lies in their ability to represent the environment through high-level image features and to provide human-readable information. However, developing a mapping and a localization method using these kinds of models is very challenging, especially if robots must deal with long-term mapping, localization, navigation, occlusions, and dynamic environments. In other words, the mobile robot has to deal with environmental appearance change, which modifies its representation of the environment. This paper proposes an indoor appearance-based mapping and a localization method for mobile robots based on the human memory model, which was used to build a Feature Stability Histogram (FSH) at each node in the robot topological map. This FSH registers local feature stability over time through a voting scheme, and the most stable features were considered for mapping, for Bayesian localization and for incrementally updating the current appearance reference view in the topological map. The experimental results are presented using an omnidirectional images dataset acquired over the long-term and considering: illumination changes (time of day, different seasons), occlusions, random removal of features, and perceptual aliasing. The results include a comparison with the approach proposed by Dayoub and Duckett (2008) [19] and the popular Bag-of-Words (Bazeille and Filliat, 2010) [35] approach. The obtained results confirm the viability of our method and indicate that it can adapt the internal map representation over time to localize the robot both globally and locally.     

Lane detection and tracking using a new lane model and distance transform

Lane detection is a significant component of driver assistance systems. Highway-based lane departure warning solutions are in the market since the mid-1990s. However, improving and generalizing vision-based lane detection remains to be a challenging task until recently. Among various lane detection methods developed, strong lane models, based on the global assumption of lane shape, have shown robustness in detection results, but are lack of flexibility to various shapes of lane. On the contrary, weak lane models will be adaptable to different shapes, as well as to maintain robustness. Using a typical weak lane model, particle filtering of lane boundary points has been proved to be a robust way to localize lanes. Positions of boundary points are directly used as the tracked states in the current research. This paper introduces a new weak lane model with this particle filter-based approach. This new model parameterizes the relationship between points of left and right lane boundaries, and can be used to detect all types of lanes. Furthermore, a modified version of an Euclidean distance transform is applied on an edge map to provide information for boundary point detection. In comparison to an edge map, properties of this distance transform support improved lane detection, including a novel initialization and tracking method. This paper fully explains how the application of this distance transform greatly facilitates lane detection and tracking. Two lane tracking methods are also discussed while focusing on efficiency and robustness, respectively. Finally, the paper reports about experiments on lane detection and tracking, and comparisons with other methods.     

基于ORB-SLAM的室内机器人定位和三维稠密地图构建

针对在室内机器人定位和三维稠密地图构建系统中,现有方法无法同时满足高精度定位、大范围和快速性要求的问题,应用具有跟踪、地图构建和重定位三平行线程的ORB-SLAM算法估计机器人三维位姿;然后拼接深度摄像头KINECT获得的三维稠密点云,提出空间域上的关键帧提取方法剔除冗余的视频帧;接着提出子地图法进一步减少地图构建的时间,最终提高算法的整体速度。实验结果表明,所提系统能够在大范围环境中准确定位机器人位置,在运动轨迹为50 m的大范围中,机器人的均方根误差为1.04 m,即误差为2%,同时整体速度为11帧/秒,其中定位速度达到17帧/秒,可以满足室内机器人定位和三维稠密地图构建的精度、大范围和快速性的要求。     

Robust omnidirectional mobile robot topological navigation system using omnidirectional vision

Robust topological navigation strategy for omnidirectional mobile robot using an omnidirectional camera is described. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot performs paths based on motion model about omnidirectional motion structure and records a set of ordered key images from omnidirectional camera. From this sequence a topological map is built based on the probabilistic technique and the loop closure detection algorithm, which can deal with the perceptual aliasing problem in mapping process. Each topological node provides a set of omnidirectional images characterized by geometrical affine and scale invariant keypoints combined with GPU implementation. Given a topological node as a target, the robot navigation mission is a concatenation of topological node subsets. In the on-line navigation stage, the robot hierarchical localizes itself to the most likely node through the robust probability distribution global localization algorithm, and estimates the relative robot pose in topological node with an effective solution to the classical five-point relative pose estimation algorithm. Then the robot is controlled by a vision based control law adapted to omnidirectional cameras to follow the visual path. Experiment results carried out with a real robot in an indoor environment show the performance of the proposed method.     

Topological map-based approach for localization and mapping memory optimization

Robotics in agriculture faces several challenges, such as the unstructured characteristics of the environments, variability of luminosity conditions for perception systems, and vast field extensions. To implement autonomous navigation systems in these conditions, robots should be able to operate during large periods and travel long trajectories. For this reason, it is essential that simultaneous localization and mapping algorithms can perform in large-scale and long-term operating conditions. One of the main challenges for these methods is maintaining low memory resources while mapping extensive environments. This work tackles this issue, proposing a localization and mapping approach called VineSLAM that uses a topological mapping architecture to manage the memory resources required by the algorithm. This topological map is a graph-based structure where each node is agnostic to the type of data stored, enabling the creation of a multilayer mapping procedure. Also, a localization algorithm is implemented, which interacts with the topological map to perform access and search operations. Results show that our approach is aligned with the state-of-the-art regarding localization precision, being able to compute the robot pose in long and challenging trajectories in agriculture. In addition, we prove that the topological approach innovates the state-of-the-art memory management. The proposed algorithm requires less memory than the other benchmarked algorithms, and can maintain a constant memory allocation during the entire operation. This consists of a significant innovation, since our approach opens the possibility for the deployment of complex 3D SLAM algorithms in real-world applications without scale restrictions.     

A Hybrid Bayesian-Frequentist Approach to SLAM

A hybrid Bayesian/ frequentist approach is presented for the Simultaneous Localization and Mapping Problem (SLAM). A frequentist approach is proposed for mapping a dense environment when the robotic pose is known and then extended to the case when the pose is uncertain. The SLAM problem is then solved in two steps: 1) the robot is localized with respect to a sparse set of landmarks in the map using a Bayes filter and a belief on the robot pose is formed, and 2) this belief on the robot pose is used to map the rest of the map using the frequentist estimator. The frequentist part of the hybrid methodology is shown to have complexity linear (constant time complexity under the assumption of bounded noise) in the map components, is robust to the data association problem and is provably consistent. The complexity of the Bayesian part is kept under control owing to the sparseness of the features, which also improves the robustness of the technique to the issue of data association. The hybrid method is tested on standard datasets on the RADISH repository.     

Iterated D-SLAM map joining: evaluating its performance in terms of consistency,accuracy and efficiency

This paper presents a new map joining algorithm and a set of metrics for evaluating the performance of mapping techniques. The input to the new map joining algorithm is a sequence of local maps containing the feature positions and the final robot pose in a local frame of reference. The output is a global map containing the global positions of all the features but without any robot poses. The algorithm is built on the D-SLAM mapping algorithm (Wang et al. in Int. J. Robot. Res. 26(2):187–204, 2007) and uses iterations to improve the estimates in the map joining step. So it is called Iterated D-SLAM Map Joining (I-DMJ). When joining maps I-DMJ ignores the odometry information connecting successive maps. This is the key to I-DMJ efficiency, because it makes both the information matrix exactly sparse and the size of the state vector bounded by the number of features.     

Toward a Unified Bayesian Approach to Hybrid Metric--Topological SLAM

This paper introduces a new approach to simultaneous localization and mapping (SLAM) that pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation that can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid discrete-continuous state space, which naturally combines metric and topological maps. There are two fundamental characteristics that set this paper apart from previous ones: 1) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem and 2) the analytical formulation of belief distributions over hybrid maps, which allows us to maintain the spatial uncertainty in large spaces more accurately and efficiently than in previous works. We also describe a practical implementation that aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30 000 m², a 2 km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches.     

Mobile-robot pose estimation and environment mapping using an extended Kalman filter

In this paper an extended Kalman filter (EKF) is used in the simultaneous localisation and mapping (SLAM) of a four-wheeled mobile robot in an indoor environment. The robot’s pose and environment map are estimated from incremental encoders and from laser-range-finder (LRF) sensor readings. The map of the environment consists of line segments, which are estimated from the LRF’s scans. A good state convergence of the EKF is obtained using the proposed methods for the input- and output-noise covariance matrices’ estimation. The output-noise covariance matrix, consisting of the observed-line-features’ covariances, is estimated from the LRF’s measurements using the least-squares method. The experimental results from the localisation and SLAM experiments in the indoor environment show the applicability of the proposed approach. The main paper contribution is the improvement of the SLAM algorithm convergence due to the noise covariance matrices’ estimation.     

面向嵌入式平台的单目ORB-SLAM稠密化建图实现

针对现有方法在机器人室内定位中无法同时满足高精度定位、快速处理及稠密地图重建的问题,在拥有跟踪、地图构建和回环检测三线程的ORB-SLAM3系统基础上设计了三维稠密地图构建算法,分别在跟踪阶段、局部光束法平差阶段（bundle adjustment,BA）和全局BA阶段,对满足需求的关键帧进行二次采样和位姿更新,然后通过关键帧和对应位姿计算得到三维点云,最终获得稠密地图。实验结果表明,所提方法在Jetson AGX Xavier嵌入式平台上对TUM数据集的定位速度达到了10.8?frame/s,均方根误差仅有0.213%,验证了该系统的高精度与快速性,可以满足机器人室内定位与建图需求。     

Efficient autonomous global localization for service robots using dual laser scanners and rotational motion

This study presents an alternative global localization scheme that uses dual laser scanners and the pure rotational motion of a mobile robot. The proposed method extracts the initial state of the robot’s surroundings to select robot pose candidates, and determines the sample distribution based on the given area map. Localization success is determined by calculating the similarity of the robot’s sensor state compared to that which would be expected at the estimated pose on the given map. In both simulations and experiments, the proposed method shows sufficient efficiency and speed to be considered robust to real-world conditions and applications.

     

Appearance-based mapping using minimalistic sensor models

This paper addresses the problem of localization and map construction by a mobile robot in an indoor environment. Instead of trying to build high-fidelity geometric maps, we focus on constructing topological maps as they are less sensitive to poor odometry estimates and position errors. We propose a modification to the standard SLAM algorithm in which the assumption that the robots can obtain metric distance/bearing information to landmarks is relaxed. Instead, the robot registers a distinctive sensor “signature”, based on its current location, which is used to match robot positions. In our formulation of this non-linear estimation problem, we infer implicit position measurements from an image recognition algorithm. We propose a method for incrementally building topological maps for a robot which uses a panoramic camera to obtain images at various locations along its path and uses the features it tracks in the images to update the topological map. The method is very general and does not require the environment to have uniquely distinctive features. Two algorithms are implemented to address this problem. The Iterated form of the Extended Kalman Filter (IEKF) and a batch-processed linearized ML estimator are compared under various odometric noise models.

Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots

Emerged as salient in the recent home appliance consumer market is a new generation of home cleaning robot featuring the capability of Simultaneous Localization and Mapping (SLAM). SLAM allows a cleaning robot not only to self-optimize its work paths for efficiency but also to self-recover from kidnappings for user convenience. By kidnapping, we mean that a robot is displaced, in the middle of cleaning, without its SLAM aware of where it moves to. This paper presents a vision-based kidnap recovery with SLAM for home cleaning robots, the first of its kind, using a wheel drop switch and an upward-looking camera for low-cost applications. In particular, a camera with a wide-angle lens is adopted for a kidnapped robot to be able to recover its pose on a global map with only a single image. First, the kidnapping situation is effectively detected based on a wheel drop switch. Then, for an efficient kidnap recovery, a coarse-to-fine approach to matching the image features detected with those associated with a large number of robot poses or nodes, built as a map in graph representation, is adopted. The pose ambiguity, e.g., due to symmetry is taken care of, if any. The final robot pose is obtained with high accuracy from the fine level of the coarse-to-fine hierarchy by fusing poses estimated from a chosen set of matching nodes. The proposed method was implemented as an embedded system with an ARM11 processor on a real commercial home cleaning robot and tested extensively. Experimental results show that the proposed method works well even in the situation in which the cleaning robot is suddenly kidnapped during the map building process.     

激光—相机系统语义栅格建图和路径规划

目的 SLAM（simultaneous localization and mapping）是移动机器人在未知环境进行探索、感知和导航的关键技术。激光SLAM测量精确，便于机器人导航和路径规划，但缺乏语义信息。而视觉SLAM的图像能提供丰富的语义信息，特征区分度更高，但其构建的地图不能直接用于路径规划和导航。为了实现移动机器人构建语义地图并在地图上进行路径规划，本文提出一种语义栅格建图方法。方法 建立可同步获取激光和语义数据的激光-相机系统，将采集的激光分割数据与目标检测算法获得的物体包围盒进行匹配，得到各物体对应的语义激光分割数据。将连续多帧语义激光分割数据同步融入占据栅格地图。对具有不同语义类别的栅格进行聚类，得到标注物体类别和轮廓的语义栅格地图。此外，针对语义栅格地图发布导航任务，利用路径搜索算法进行路径规划，并对其进行改进。结果 在实验室走廊和办公室分别进行了语义栅格建图的实验，并与原始栅格地图进行了比较。在语义栅格地图的基础上进行了路径规划，并采用了语义赋权算法对易移动物体的路径进行对比。结论 多种环境下的实验表明本文方法能获得与真实环境一致性较高、标注环境中物体类别和轮廓的语义栅格地图，且实验硬件结构简单、成本低、性能良好，适用于智能化机器人的导航和路径规划。     

Handling and pushing objects using unmanned guided vehicles

This paper aims handling box-shape objects combining mapping, searching, and path planning techniques. The proposal enables a mobile robot to push objects autonomously from random positions to a final destination. Laser scanner data are used to build up a 2D map, which aids the objects’ identification in the scene. Next, a topological map is created and Bézier curves provide suitable paths taking into account the position of the robot, objects and final destination. Then, Dijkstra’s algorithm finds the optimal route. Finally, simulations are run in V-REP + Matlab, and real experiments validate the proposal, which demonstrates quite efficient for environments without occlusion of the objects to be transported.     

Model predictive control for fast reaching in clutter

A key challenge for haptically reaching in dense clutter is the frequent contact that can occur between the robot’s arm and the environment. We have previously used single-time-step model predictive control (MPC) to enable a robot to slowly reach into dense clutter using a quasistatic mechanical model. Rapid reaching in clutter would be desirable, but entails additional challenges due to dynamic phenomena that can lead to higher forces from impacts and other types of contact. In this paper, we present a multi-time-step MPC formulation that enables a robot to rapidly reach a target position in dense clutter, while regulating whole-body contact forces to be below a given threshold. Our controller models the dynamics of the arm in contact with the environment in order to predict how contact forces will change and how the robot’s end effector will move. It also models how joint velocities will influence potential impact forces. At each time step, our controller uses linear models to generate a convex optimization problem that it can solve efficiently. Through tens of thousands of trials in simulation, we show that with our dynamic MPC a simulated robot can, on average, reach goals 1.4 to 2 times faster than our previous controller, while attaining comparable success rates and fewer occurrences of high forces. We also conducted trials using a real 7 degree-of-freedom (DoF) humanoid robot arm with whole-arm tactile sensing. Our controller enabled the robot to rapidly reach target positions in dense artificial foliage while keeping contact forces low.     

Extensions to vector field SLAM for large environments

Vector field SLAM is a framework for localizing a mobile robot in an unknown environment by learning the spatial distribution of continuous signals such as those emitted by WiFi or active beacons. In our previous work we showed that this approach is capable of keeping a robot localized in small to medium sized areas, e.g. in a living room, where four continuous signals of an active beacon are measured (Gutmann et al., 2012). In this article we extend the method to larger environments up to the size of a complete home by deploying more signal sources for covering the expanded area. We first analyze the complexity of vector field SLAM with respect to area size and number of signals and then describe an approximation that divides the localization map into decoupled sub-maps to keep memory and run-time requirements low. We also describe a method for re-localizing the robot in a vector field previously mapped. This enables a robot to resume its navigation after it has been kidnapped or paused and resumed. The re-localization method is evaluated in a standard test environment and shows an average position accuracy of 10 to 35 cm with a localization success rate of 96 to 99%. Additional experimental results from running the system in houses of up to 125 m² demonstrate the performance of our approach. The presented methods are suitable for commercial low-cost products including robots for autonomous and systematic floor cleaning.     

Lane-keeping assistance control algorithm using differential braking to prevent unintended lane departures

This paper describes a hierarchical lane keeping assistance control algorithm for a vehicle. The proposed control strategy consists of a supervisor, an upper-level controller and a lower-level controller. The supervisor determines whether lane departure is intended or not, and whether the proposed algorithm is activated or not. To detect driver′s lane change intention, the steering behavior index has been developed incorporating vehicle speed and road curvature. To validate the detection performance on the lane change intention, full-scale simulator tests on a virtual test track (VTT) are conducted under various driving situations. The upper-level controller is designed to compute the desired yaw rate for the lane departure prevention, and for the guidance with ride comfort. The lower-level controller is designed to compute the desired yaw moment in order to track the desired yaw rate, and to distribute it into each tire′s braking force in order to track the desired yaw moment. The control allocation method is adopted to distribute braking forces under the actuator’s control input limitation. The proposed lane keeping assistance control algorithm is evaluated with human driver model-in-the-loop simulation and experiments on a real vehicle.