The scan matching is widely used for localization and mapping of mobile robots. In this paper, a direction of data point in the scan is approximated and this is incorporated into the scan matching algorithm to improve the performance. The direction of data point is the normal direction of the least squares fitted line based on neighbors of the data point. Owing to this incorporation, the performance of the scan matching can be improved. The number of iterations decreases, and the tolerance against a large relative-rotation between scans increases. Using real sensor data of a laser range finder, experimental results verify the performance of the proposed algorithm, the direction augmented probabilistic scan matching.

This paper presents a method of topological localization in a home environment using only low-cost sonar sensors. The proposed method utilizes a relative motion model to obtain a prior information of node probability. Moreover, the size of a local gridmap used for gridmap matching is determined adaptively by an entropy test of node probability. The proposed method results in a reliable and convergent localization result even under the uncertainty and sparsity of sonar sensros. Experimental results verify performance of the proposed topological localization in a real home environment.

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.

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.

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.

This paper addresses the problem of Simultaneous Localization and Map Building (SLAM) using a Neural Network aided Extended Kalman Filter (NNEKF) algorithm. Since the EKF is based on the white noise assumption, if there are colored noise or systematic bias error in the system, EKF inevitably diverges. The neural network in this algorithm is used to approximate the uncertainty of the system model due to mismodeling and extreme nonlinearities. Simulation results are presented to illustrate the proposed algorithm NNEKF is very effective compared with the standard EKF algorithm under the practical condition where the mobile robot has bias error in its modeling and environment has strong uncertainties. In this paper, we propose an algorithm which enables a biased control input in vehicle model using neural network.

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

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