With rapid advancements in the area of mobile robotics and industrial automation, a growing need has arisen towards accurate navigation and localization of moving objects. Camera-based motion estimation is one such technique which is gaining huge popularity owing to its simplicity and use of limited resources in generating motion path. In this chapter, an attempt is made to introduce this topic for beginners covering different aspects of vision-based motion estimation task. The theoretical section provides a brief on different computer vision fundamentals specific to pose estimation task followed by a systematic discussion on the visual odometry (VO) schemes under different categories. The evolution of VO schemes over last few decades is discussed under two broad categories, that is, geometric and non-geometric approaches. The geometric approaches are further detailed under three different classes, that is, feature-based, appearance-based, and a hybrid of feature and appearance based schemes. The non-geometric approach is one of the recent paradigm shift from conventional pose estimation technique and is discussed in a separate section. Towards the end, a list of different datasets for visual odometry and allied research areas are provided for a ready reference.

Motion Estimation Made Easy: Evolution and Trends in Visual Odometry

In the last years visual odometers have been improved considerably, but there is still a certain lack of robustness and reliability when used to navigate Autonomous Underwater Vehicles (AUV) in complex underwater scenarios, such as habitats colonized with Posidonia oceanica or Zostera noltii. The work presented in this paper goes one step beyond the current solutions, improving a state of the art approach to be applied in underwater applications. Tests conducted with real marine visual data grabbed in waters of the Balearic Islands from a bottom-looking camera installed on an AUV, show the progress in the vehicle displacement estimation and its viability to be used online. 

Evolving Visual Odometry for Autonomous Underwater Vehicles

Visual odometry is a popular technique used to estimate motion in GPS-challenged environment, whose accuracy depends on the features extracted from the images. In past attempts to improved feature distinctiveness, these features have become complex and lengthier, requiring more storage space and computational power for matching. In this paper, an attempt is made toward reducing the length of these feature descriptors while maintaining a similar accuracy in pose estimation. Elimination of feature indices based on variance analysis on feature column sets is proposed and experimented in this paper. The features with reduced descriptor length are applied over the 3D-2D visual odometry pipeline and experimented on KITTI dataset for evaluating its efficacy. The proposed scheme of variance-based descriptor length reduction is found to reduce the overall time taken by the motion estimation framework while estimating the transformation with similar accuracy as that with full-length feature vector.

Descriptor-Length Reduction Using Low-Variance Filter for Visual Odometry

Visual odometry is a well-known technique that is used to compute the rotation and translation of any moving vehicle with the help of camera mounted over it. This task of vision-based navigation is used for different applications such as autonomous navigation, motion tracking and obstacle detection, etc. This paper illustrates an approach for estimating vehicle motion by detecting and matching scale invariant SURF feature over consequent image frames. These set of matched feature are passed through an outlier removal and inlier selection methodology sequentially in order to remove the inconsistent features. Additionally, the proposed scheme incorporates the bucketing technique to ensure spatial distribution of feature in the overall image space. The proposed scheme of inlier selection-cum-outlier rejection has been applied on the KITTI dataset available online and is found to work satisfactorily as compared to the individual outlier rejection or inlier selection mechanism.

Rohan More

Papers

Improved pose estimation by inlier refinement for visual odometry