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Abstract: Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems, such as visual odometry (VO) and simultaneous localization and mapping (SLAM), suffer from two shortcomings: a drift problem caused by accumulated localization error, and erroneous motion estimation due to illumination variation and moving objects. In this paper, we propose an enhanced VO by introducing a panoramic camera into the traditional stereo-only VO system. Benefiting from the 360° field of view, the panoramic camera is responsible for three tasks: (1) detecting road junctions and building a landmark library online; (2) correcting the robot’s position when the landmarks are revisited with any orientation; (3) working as a panoramic compass when the stereo VO cannot provide reliable positioning results. To use the large-sized panoramic images efficiently, the concept of compressed sensing is introduced into the solution and an adaptive compressive feature is presented. Combined with our previous two-stage local binocular bundle adjustment (TLBBA) stereo VO, the new system can obtain reliable positioning results in quasi-real time. Experimental results of challenging long-range tests show that our enhanced VO is much more accurate and robust than the traditional VO, thanks to the compressive panoramic landmarks built online.

The paper presents a method for enhancing the visual odometry using two kinds of vision sensors, like stereo cameras and panoramic (360 degree-omnidirectional) camera. The proposed method aims to reduce the error of motion estimation based on matching the landmarks in panoramic images with database in the library as the robot is revisiting the scenes, which focuses on special scenes such as road junctions and buildings. Authors utilize the panoramic camera for estimating the azimuthal rotation of robot when the stereo VO (visual odometry) could not sufficiently provide reliable position estimation results. The experimental results demonstrate the efficiency and effectiveness of the proposed method under varied conditions. It would be a good idea to compare the result with other landmark matching localization schemes. This comparison would be more useful if the authors are able to quantify the benefit (may be in terms of frequency and accuracy) of the proposed work. The repeatability of the proposed scheme should also be commented. In general, the paper is fairly well written. Overall quality of the research is acceptable.

基于在线建立与匹配压缩全景路标的增强型视觉里程计

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