POSE ESTIMATION OF A MOVING CAMERA WITH LOW-COST, MULTI-GNSS DEVICES

Without additional prior information, the pose of a camera estimated with computer vision techniques is expressed in a local coordinate frame attached to the camera’s initial location. Albeit sufficient in many cases, such an arbitrary representation is not convenient for employment in certain applications and has to be transformed to a coordinate system external to the camera before further use. Assuming a camera that is firmly mounted on a moving platform, this paper describes a method for continuously tracking the pose of that camera in a projected coordinate system. By combining exterior orientation from a known target with incremental pose changes inferred from accurate multi-GNSS positioning, the full 6 DoF pose of the camera is updated with low processing overhead and without requiring the continuous visual tracking of ground control points. Experimental results of applying the proposed method to a moving vehicle and a mobile port crane are reported, demonstrating its efficacy and potential.

Linear Approach for Initial Recovery of the Exterior Orientation Parameters of Randomly Captured Images by Low-Cost Mobile Mapping Systems

In this paper, we present a novel linear approach for the initial recovery of the exterior orientation parameters (EOPs) of images. Similar to the conventional Structure from Motion (SfM) algorithm, the proposed approach is based on a two-step strategy. In the first step, the relative orientation of all possible image stereo-pairs is estimated. In the second step, a local coordinate frame is established, and an incremental image augmentation process is implemented to reference all the remaining images into a local coordinate frame. Since our approach is based on a linear solution for both the relative orientation estimation as well as the initial recovery of the image EOPs, it does not require any initial approximation for the optimization process. Another advantage of our approach is that it does not require any prior knowledge regarding the sequence of the image collection procedure, therefore, it can handle a set of randomly collected images in the absence of GNSS/INS information. In order to illustrate the feasibility of our approach, several experimental tests are conducted on real datasets captured in either a block or linear trajectory configuration. The results demonstrate that the initial image EOPs obtained are accurate and can serve as a good initialization for an additional bundle adjustment process.

Representation and recognition of the movements of shapes

The problems posed by the representation and recognition of the movements of 3-D shapes are analysed. A representation is proposed for the movements of shapes that lie within the scope of the Marr & Nishihara (1978) 3-D model representation of static shapes. The basic problem is how to segment a stream of movement into pieces, each of which can be described separately. The representation proposed here is based upon segmenting a movement at moments when a component axis, e. g. an arm, starts to move relative to its local coordinate frame (here the torso). For example, walking is divided into a segment of the stationary states between each swing of the arms and legs, and the actual motions between the stationary points (relative to the torso, not the ground). This representation is called the state─motion─state (SMS) moving shape representation, and several examples of its application are given.