Tae-Kyeong Lee

A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot

Abstract This paper describes a geometrically constrained Extended Kalman Filter (EKF) framework for a line feature based SLAM, which is applicable to a rectangular indoor environment. Its focus is on how to handle sparse and noisy sensor data, such as PSD infrared sensors with limited range and limited number, in order to develop a low-cost navigation system. It has been applied to a vacuum cleaning robot in our research. In order to meet the real-time objective with low computing power, we develop an efficient line feature extraction algorithm based upon an iterative end point fit (IEPF) technique assisted by our constrained version of the Hough transform. It uses a geometric constraint that every line is orthogonal or parallel to each other because in a general indoor setting, most furniture and walls satisfy this constraint. By adding this constraint to the measurement model of EKF, we build a geometrically constrained EKF framework which can estimate line feature positions more accurately as well as allow their covariance matrices to converge more rapidly when compared to the case of an unconstrained EKF. The experimental results demonstrate the accuracy and robustness to the presence of sensor noise and errors in an actual indoor environment.

Online complete coverage path planning for mobile robots based on linked spiral paths using constrained inverse distance transform

This paper presents a sensor-based online coverage path planning algorithm guaranteeing a complete coverage of unstructured planar environments by a mobile robot. The proposed complete coverage algorithm abstracts the environment as a union of robot-sized cells and then uses a spiral filling rule. It can be largely classified as an approximate cellular decomposition approach as defined by Choset. In this paper, we first propose a special map coordinate assignment scheme based on active wall-finding using the history of sensor readings, which can drastically reduce the number of turns on the generated coverage path. Next, we develop an efficient path planner to link the simple spiral paths using the constrained inverse distance transform that we introduced the first time. This planner selects the next target cell which is at the minimal path length away from the current cell among the remaining non-contiguous uncovered cells while at the same time, finding the path to this target to save both the memory and time which are important concern in embedded robotics. Experiments on both simulated and real cleaning robots demonstrate the practical efficiency and robustness of the proposed algorithm.

Indoor mapping using planes extracted from noisy RGB-D sensors

This paper presents a fast and robust plane feature extraction and matching technique for RGB-D type sensors. We propose three algorithm components required to utilize the plane features in an online Simultaneous Localization and Mapping (SLAM) problem: fast plane extraction, frame-to-frame constraint estimation, and plane merging. For the fast plane extraction, we estimate local surface normals and curvatures by a simple spherical model and then segment points using a modified flood fill algorithm. In plane parameter estimation, we suggest a new uncertainty estimation method which is robust against the measurement bias, and also introduce a fast boundary modeling method. We associate the plane features based on both the parameters and the spatial coverage, and estimate the stable constraints by the cost function with a regulation term. Also, our plane merging technique provides a way of generating local maps that are useful for estimating loop closure constraints. We have performed real-world experiments at our lab environment. The results demonstrate the efficiency and robustness of the proposed algorithm.

Smooth coverage path planning and control of mobile robots based on high-resolution grid map representation

Abstract This paper presents a new approach to a time and energy efficient online complete coverage solution for a mobile robot. While most conventional approaches strive to reduce path overlaps, this work focuses on smoothing the coverage path to reduce accelerations and yet to increase the average velocity for faster coverage. The proposed algorithm adopts a high-resolution grid map representation to reduce directional constraints on path generation. Here, the free space is covered by three independent behaviors: spiral path tracking, wall following control, and virtual wall path tracking. Regarding the covered region as a virtual wall, all the three behaviors adopt a common strategy of following the (physical or virtual) wall or obstacle boundaries for close coverage. Wall following is executed by a sensor-based reactive path planning control process, whereas the spiral (filling) path and virtual wall path are first modeled by their relevant parametric curves and then tracked via dynamic feedback linearization. For complete coverage, these independent behaviors are linked through a new path linking strategy, called a coarse-to-fine constrained inverse distance transform (CFCIDT). CFCIDT reduces the computational cost compared to the conventional constrained inverse distance transform (CIDT), which applies a region growing starting from the current robot position to find the nearest unexplored cell as well as the shortest path to it while constraining the search space. As for experimental validation, performance of the proposed algorithm is compared to those of conventional coverage techniques to demonstrate its completeness of coverage, energy and time efficiency, and robustness to the environment shape or the initial robot pose.

Sector-based maximal online coverage of unknown environments for cleaning robots with limited sensing

Abstract Although cleaning robots have been increasingly popular in home environments, their coverage rate and performance has not been very impressive to their users, thus often hampering their user acceptance. Many complete coverage algorithms developed so far usually mandate the robot to have a sophisticated navigation system for precise localization. This requires the use of high-cost sensors as well as high computational power — thus not suitable for home environments. This paper presents a novel integrated coverage strategy for low-cost cleaning robots, yet demonstrating respectable coverage performance in most unknown environments. The proposed algorithm can efficiently cope with hardware limitations ranging from low computational power to numerous sensing problems arising from limited range, sparse data, and detection uncertainty. To facilitate a viable solution that can cope with these limitations, we first make two assumptions on the home environment — rectilinear and closed, which seems to be met in most of our home environments. Next, in order to effectively circumvent poor localization (low precision positioning), we decompose the space into sectors, with each sector being small enough to have reasonable localization accuracy within itself. Overall, the final outcome is a novel online coverage strategy that performs simultaneous exploration, incremental sector creation, sector cleaning, and localization, with the intention of maximizing performance with minimal sensing. Both simulation and real-world experiments validate the efficiency of our approach.

Complete coverage algorithm based on linked smooth spiral paths for mobile robots

This paper presents an on-line complete-coverage path planning algorithm for mobile robots based on approximate cellular decomposition, which abstracts the target environment using grid. Most existing grid-based coverage algorithms have a common problem of constrained mobility which degrades the efficiency of the coverage task by inducing zigzag like path. In this paper, we propose a new complete coverage path generation algorithm, linked-smooth-spiral-path (LSSP), which removes the constraint on mobility by adopting a high-resolution grid-map representation of the environment and a cardinal-spline curve-model to generate a smooth spiral coverage path. We define a new performance measure for coverage tasks, which quantifies the smoothness of the coverage path. Simulation results demonstrate the improved coverage performance of the proposed algorithm compared to other existing grid-based coverage algorithms.

Generating topological map from occupancy grid-map using virtual door detection

This paper proposes a method for a cleaning robot to generate a topological map from an occupancy grid-map. Virtual door is defined as the candidates of real door, and the virtual doors are detected as edges of the topological map by extracting corner features from the occupancy grid-map; using this method, an initial topological map is generated, which consists of nodes and edges. The final topological map is generated using a genetic algorithm to merge the nodes and reduce the edges. As a result, the generated topological map consists of nodes divided by virtual doors and edges located in real doors. The proposed methods provide a topological map for the user interaction and the cleaning robot, and the topological map can be used to plan more efficient motion including room-to-room path planning and covering each room.

Integrated On-Line Localization, Mapping and Coverage Algorithm of Unknown Environments for Robotic Vacuum Cleaners Based on Minimal Sensing

This paper presents a new complete coverage algorithm of a robotic vacuum cleaner (RVC) with a low-cost sensor in an unknown environment. To achieve complete coverage, the RVC must have navigation systems for precise position estimation with localization and a prior map or a map using information that has been continuously collected from the environment. To do this, two-dimensional laser range finders and vision sensors are becoming increasingly popular in mobile robotics, and various methods using sensors like these have been introduced by many researchers. However, it is difficult to apply the methods to sensors used in most RVCs due to their constraints. In this paper, we present a new method applied to most RVCs. For developing the method, we considered the two main problems of maintaining low computational load, and coping with low-cost sensor systems with limited range, detection uncertainty and measurement error. To solve the problems, we propose an assumption that major structures of an indoor environment are rectilinear, and can be represented by sets of parallel and perpendicular lines. Then we derive an algorithm that uses this assumption to map the environment, localize the robot and plan the coverage path with a new cellular decomposition approach. Simulation and experiments verify that the proposed method guarantees complete coverage.

A hierarchical RBPF SLAM for mobile robot coverage in indoor environments

In this paper, we develop a new framework for simultaneous localization and mapping (SLAM) based on Rao-Blackwellized particle filters (RBPF), that can be applied to floor-cleaning robots which are equipped with sparse and short-range sensors. To overcome the sensor limitations, the entire region is divided into several local maps, which are assumed to be independent to each other. The local maps are estimated by a local RBPF SLAM, and then the trajectory of the local map origin is estimated by a global RBPF SLAM. To compensate for the severe sensing limitations, we also adopt the assumption that the indoor environments consist of many orthogonal lines. This assumption significantly enhances the filter performance. The proposed SLAM framework is combined with a coverage path planning algorithm, and the resulting robot system is capable of online simultaneous coverage and SLAM. The algorithm was embedded into a real mobile robot platform and tested in a real home environment to assess the robustness of the proposed method.

Feature Map Based Complete Coverage Algorithm for a Robotic Vacuum Cleaner

Abstract The coverage ability is one of essential techniques for the Ro botic Vacuum Cleaner (RVC). Most of the RVCs rely on random or regular pattern movement to cover a target space due to the technical difficulties to implement localization and map and constraints of hardwares such as contr oller and sensors. In this paper, we consider two main issues which are low computational load and using sensors with very limited sensing capabilities. First, in our approach, computing procedures to build map and detect the RVCs position are minimized by simplifying data obtained from sensors. To reduce computational load, it needs s imply presenting an environment with objects of various shapes. Another isuue mentioned above is regarded as on e of the most important problems in our approach, because we consider that many RVCs use low-cost sensor systems such as an infrared sensor or ultrasonic sensor with limited capabilities in limited range, detection uncertainty, measurement noise, etc. Methods presented in this paper are able to apply to general RVCs equipped with these sen sors. By both simulation and real experiment, we evaluate our method and verify that the proposed method guarant ees a complete coverage.