In this paper, we present a vision-based path following algorithm for a non-holonomic wheeled platform. The algorithm is based on choosing control actions that minimise the value of a potential field cost function calculated directly from the image plane. The algorithm is suitable for teach and replay or leader follower implementations where the desired path is represented as a collection of images. The algorithm computes the cost function based on the relative bearings of features matched between the current and previously observed images. A forward prediction step is then used to determine the control action that will lead to the greatest reduction in the cost function. The algorithm is demonstrated on a 400 m path in an outdoor environment where the accuracy is shown to be similar to that of differential GPS.

In this work, we propose a novel RFID-based hybrid metric-topological Simultaneous Localization and Mapping (SLAM) algorithm which enables autonomous navigation in GPS-denied environments. A method based on the normalized-cut is proposed for online clustering of strongly connected Radio Frequency Identification (RFID) tags to form topological nodes. A particle filter together with a sensor model which characterizes the received signal strength (RSS) as well as the tag detection probability is used to create metric submaps for each topological node. The hybrid framework is highly scalable, simplifies path planning and promises precision and robustness. The algorithm requires only odometry and RFID measurements to localize the RFID tags with a relative accuracy of approximately 0.3 meters. The ideas presented here are supported by experimental results.

In this paper we present a vision-based path following algorithm for a non-holonomic wheeled platform capable of keeping the vehicle on a desired path using only a single camera. The algorithm is suitable for teach and replay or leader-follower implementations where the desired path is represented by a collection of images obtained along the path. The algorithm makes use of the 1D trifocal tensor to estimate parameters required for the path following controller through a structure from motion approach. Our algorithm provides the benefits of a position-based visual servoing method without having to explicitly recover the 3D structure of the environment. By using the trifocal tensor, the proposed algorithm overcomes several problems usually associated with visual servoing techniques. The singularities commonly encountered with image-based visual servoing and epipolar methods are eliminated; and the unknown scale problem is also resolved by incorporating the scale into the control parameters. This simultaneously removes the velocity dependence of the controller gains and the need for odometric sensors on the platform. In addition, we also propose a novel method of resolving the ambiguities often associated with structure from motion when using the 1D trifocal tensor. The proposed algorithm is validated using both simulated and experimental results where robustness to a large degree of feature noise is demonstrated.

In this paper we present a topological map building algorithm based on a Vocabulary Tree that is robust to features present in dynamic or similar environments. The algorithm recognises incorrect loop closures, not supported by the odometry, and uses this information to update the feature weights in the tree to suppress further associations from these features. Two methods of adjusting these feature entropies are proposed, one decreasing entropy related to incorrect features in a uniform manner and the other proportional to the contribution of the said feature. Preliminary results showing the performance of the proposed method are presented where it is found that by adjusting the feature entropies, the number of incorrect associations can be reduced while improving the quality of the correct matches.