Jinwoo Choi

Data Association Using Visual Object Recognition for EKF-SLAM in Home Environment

Reliable data association is crucial to localization and map building for mobile robot applications. For that reason, many mobile robots tend to choose vision-based SLAM solutions. In this paper, a SLAM scheme based on visual object recognition, not just a scene matching, in home environment is proposed without using artificial landmarks. For the object-based SLAM, the following algorithms are suggested: 1) a novel local invariant feature extraction by combining advantages of multi-scale Harris corner as a detector and its SIFT descriptor for natural object recognition, 2) the RANSAC clustering for robust object recognition in the presence of outliers and 3) calculating accurate metric information for SLAM update. The proposed algorithms increase robustness by correct data association and accurate observation. Moreover, it also can be easily implemented real-time by reducing the number of representative landmarks, i.e. objects. The performance of the proposed algorithm was verified by experiments using EKF-SLAM with a stereo camera in home-like environments, and it showed that the final pose error was bounded after battery-run-out autonomous navigation for 50 minutes

Robust sonar feature detection for the SLAM of mobile robot

Sonar sensor is an attractive tool for the SLAM of mobile robot because of their economic aspects. This cheap sensor gives relatively accurate range readings if disregarding angular uncertainty and specular reflections. However, these defects make feature detection difficult for the most part of the SLAM. This paper proposes a robust sonar feature detection algorithm. This algorithm gives feature detection methods for both point features and line features. The point feature detection method is based on the TBF (Wijk and Christensen, 2000) scheme. Moreover, three additional processes improve the performance of feature detection as follows; 1) stable intersections; 2) efficient sliding window update; and 3) removal of the false point features on the wall. The line feature detection method is based on the basic property of adjacent sonar sensors. Along the line feature, three adjacent sonar sensors give similar range readings. Using this sensor property, we propose a novel algorithm for line feature detection, which is simple and the feature can be obtained by using only current sensor data. The proposed feature detection algorithm gives a good solution for the SLAM of mobile robots because it gives an accurate feature information for both the point and line features even with sensor errors. Furthermore, a sufficient number of features are available to correct mobile robot pose. Experimental results of the EKF-based SLAM demonstrate the performance of the proposed feature detection algorithm in a home-like environment.

Incremental topological modeling using sonar gridmap in home environment

This paper presents a method of topological modeling in home environments using only low-cost sonar sensors. The proposed method constructs a topological model using sonar gridmap by extracting subregions incrementally. A confidence for each occupied grid is evaluated to obtain reliable regions in a local gridmap, and a convexity measure is used to extract subregions automatically. Through these processes, the topological model is constructed without predefining the number of subregions in advance and the extracted subregions are guaranteed the convexity. Experimental results verify the performance of proposed method in real home environment.

State estimation with delayed measurements considering uncertainty of time delay

State estimation problem with time delayed measurements is addressed. In dynamic system with noise, after taking measurements, it often requires some time until that is available in a filter. A filter not considering this time delay cannot be used since a current measurement is related with a past state. These delayed measurements problem is solved with augmented state Kalman filter, and uncertainty of the delayed time is also resolved based on the probability distribution of the delay. The proposed method is analyzed by a simple example, and its consistency is verified.

An efficient mobile robot path planning using hierarchical roadmap representation in indoor environment

This paper describes a practical approach to solve a path planning problem in a home environment. The proposed approach incrementally constructs the hierarchical roadmap which has a multi-layered structure using a sonar grid map when a mobile robot navigates in unexplored area. The hierarchical roadmap can almost completely cover the traversable areas in the environment. The mobile robot path planner using the hierarchical roadmap can efficiently search for appropriate paths under the limited computing power and time by reducing the search space size. The benefits of the hierarchical roadmap representation were verified by experiments in a home environment.

Metric SLAM in Home Environment with Visual Objects and Sonar Features

To increase the intelligence of mobile robot, various sensors need to be fused effectively to cope with uncertainty induced from both environment and sensors. Combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency vision based SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes: extracting robust point and line features from sonar data, and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. Fusing sonar features and visual objects through EKF-based SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in home environment. The performance of the proposed algorithm was verified by experiments in home environment with dynamic obstacles

A Practical Solution to SLAM and Navigation in Home Environment

To implement an autonomous mobile robot, both SLAM and task based navigation algorithms should be performed successfully. Especially, the performance of the estimation while the mobile robot performs task based navigation should be guaranteed. For this purpose, we integrate a SLAM method and a navigation algorithm for practical autonomous mobile robot. The SLAM method combines sonar sensors and stereo camera together using the EKF-based SLAM. Fusing sonar features and visual objects can give correct data association via object recognition and high frequency update via sonar features. The navigation algorithm consists of global and local path planner when the goal position is given. The global path planner uses modified A* algorithm and it gives the mobile robot enough opportunity to detect the registered landmarks during moving to the goal position. As a local path planner, for safe obstacle avoidance, we propose circle following (CF) algorithm. The performance of the proposed algorithm was verified by experiments in home environment with dynamic obstacles

Topological modeling and classification in home environment using sonar gridmap

This paper presents a method of topological representation and classification in home environment using only low-cost sonar sensors. Approximate cell decomposition and normalized graph cut are applied to sonar gridmap to extract graphical model of the environment. The extracted model represents spatial relation of the environment appropriately by segmenting several subregions. Moreover, node classification is achieved by applying template matching method to a local gridmap. Rotational invariant matching is used to obtain candidate location for each node and the true node can be classified by considering detail distance information. The proposed method extracts well-structured topological model of the environment and classification also results in reliable matching even under the uncertain and sparse sonar data. Experimental results verify the performance of proposed environmental modeling and classification in real home environment.

Correlation-Based Scan Matching Using Ultrasonic Sensors for EKF Localization

Abstract This paper presents a localization method for a mobile robot equipped with only low-cost ultrasonic sensors. Correlation-based Hough scan matching was used to obtain the robot’s pose without any predefined geometric features. A local grid map and a sound pressure model of ultrasonic sensors were used to acquire reliable scan results from uncertain and noisy ultrasonic sensor data. The robot’s pose was measured using correlation-based Hough scan matching, and the covariance was calculated. Localization was achieved by fusing the measurements from scan matching with the robot’s motion model through the extended Kalman filter. Experimental results verified the performance of the proposed localization method in a real home environment.

Experimental results of real-time sonar-based underwater localization using landmarks

This paper presents experimental results of a realtime sonar-based localization technique using the probability-based landmark-recognition method. Sonar based localization is used for the navigation of unmanned underwater vehicle (UUVs). Inertial sensors such as inertial measurement unitss (IMUs), Doppler velocity logs (DVLs), and external information obtained from sonar are combined using the extended Kalman filter (EKF) technique to obtain the navigation information. We estimate the vehicle location using inertial sensor data, and it is corrected using sonar data, which provides the relative position between the vehicle and a landmark placed on the bottom. To verify the suitability of the proposed method, we perform experiments in a basin environment using the UUV, “yShark”.