A Simplified Solution to Motion Estimation Using an Omnidirectional Camera and a 2-D LRF Sensor

Recently, omnidirectional camera-based motion estimation has been improved as a result of planar-motion assumption, which helps reduce the computational time. In practice in outdoor terrains, vehicle motion does not satisfy this assumption. Motivated by this problem, this paper proposes a method that uses the minimal set of parameters of geometric constraint for estimating the pseudo-three-dimensional motion of the vehicle based on an omnidirectional camera and a laser range finder (LRF). To reduce the number of the parameters for accelerating the computational speed, it is supposed that the vehicle moves under the nonholonomic four-wheel motion model, which requires constraints between rotation and translation components. The method consists of two stages. First, the LRF is used to estimate the orientation and the translation magnitude of the vehicle movement. Second, a pair of corresponding points in sequential images is used along with the results of the first stage to estimate the motion of the vehicle. Furthermore, this paper also presents a closed-form solution for problem solving. The experimental results, using synthetic and real data in different terrain conditions, demonstrate that the proposed method provides the higher accuracy with the lower computational time as compared with the state-of-the-art methods.