Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In order to fuse camera and odometer measurements,  first need to estimate their relative transformation through the so-called odometer-camera extrinsic calibration. ,  present a two-step analytical least-squares solution for the extrinsic odometer-camera calibration that (i) is not iterative and finds the least-squares optimal solution without any initialization, and (ii) does not require any special hardware or the presence of known landmarks in the scene. Specifically, in the first step,  estimate a subset of the 3D relative rotation parameters by analytically minimizing a least-squares cost function.  then back-substitute these estimates in the measurement constraints, and determine the rest of the 3D transformation parameters by analytically minimizing a second least-squares cost function. Simulation and experimental results are presented that validate the efficiency and accuracy of the proposed algorithm.
['Chao X. Guo', 'Faraz M. Mirzaei', 'Stergios I. Roumeliotis']
I am writing a research paper on a idea An analytical least-squares solution to the odometer-camera extrinsic calibration problem
[{"from": "human", "value": "I am writing a research paper on a idea An analytical least-squares solution to the odometer-camera extrinsic calibration problem"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In order to fuse camera and odometer measurements,  first need to estimate their relative transformation through the so-called odometer-camera extrinsic calibration. ,  present a two-step analytical least-squares solution for the extrinsic odometer-camera calibration that (i) is not iterative and finds the least-squares optimal solution without any initialization, and (ii) does not require any special hardware or the presence of known landmarks in the scene. Specifically, in the first step,  estimate a subset of the 3D relative rotation parameters by analytically minimizing a least-squares cost function.  then back-substitute these estimates in the measurement constraints, and determine the rest of the 3D transformation parameters by analytically minimizing a second least-squares cost function. Simulation and experimental results are presented that validate the efficiency and accuracy of the proposed algorithm."}]