A Structureless Approach for Visual Odometry

A local bundle adjustment is an important proce- dure to improve the accuracy of a visual odometry solution. However, it is computationally very expensive as it jointly optimize all the poses of cameras and locations of map points. To reduce the computational complexity of a local bundle adjustment, the state-of-the-art algorithms [1]–[3] were proposed to manipulate the map point variables, using extra matrix operations, from their linearized optimization solutions. Instead of relying on complex matrix manipulations, this paper proposes a novel way of addressing this complexity issue-we represent a map point as a function of two camera poses, and uses the triangulated location of the map point when needed. Our method is more efficient than ones in the full-SLAM formulation in solving the visual odometry problem in that 1) the complexity of our solution is lower than those of the state-of-the-art methods, 2) no extra matrix operations required to eliminate map points, 3) no need guesses on map points' initial locations. Experiemental results, through simulated experiments and experiments with the KITTI dataset, demonstrated that our results are more accurate than those of a full-SLAM approach with lower runtime complexities.