Image feature-based ego-motion estimation has been dominating the development of visual odometry (VO) visual simultaneously localisation and mapping (V-SLAM) and structure-from-motion (SfM) for several years. The detection extraction or representation of image features play crucial roles when solving camera pose estimation problems in terms of accuracy and computational cost. In this paper we review three popular classes of image features namely SIFT SURF and ORB as well as the recently proposed A-KAZE features. These image features are evaluated using the KITTI benchmark dataset to conclude about reasons for deciding about the selection of a particular feature when implementing monocular visual odometry.

This paper presents a fully-automated method to establish a calibration dataset from on-site scans and recalibrate the intrinsic parameters of a spinning multi-beam 3-D scanner. The proposed method has been tested on a Velodyne HDL-64E S2 LiDAR system, which contains 64 rotating laser rangefinders. By time series analysis, we found that the collected range data have random measurement errors of around ±25 mm. In addition, the layered misalignment of scans among the rangefinders, which is identified as a systematic error, also increases the difficulty to accurately locate planar surfaces. We propose a temporal-spatial range data fusion algorithm, along with a robust RANSAC-based plane detection algorithm to address these issues. Furthermore, we formulate an alternative geometric interpretation of sensory data using linear parameters, which is advantageous for the calibration procedure. The linear representation allows the proposed method to be generalized to any LiDAR system that follows the rotating beam model. We also confirmed in this paper, that given effective calibration datasets, the pre-calibrated factory parameters can be further tuned to achieve significantly improved performance. After the optimization, the systematic error is noticeable lowered, and evaluation shows that the recalibrated parameters outperform the factory parameters with the RMS planar errors reduced by up to 49%.

The calibration of a projector-camera system is an essential step toward accurate 3-D measurement and environment-aware data projection applications, such as augmented reality. In this paper we present a two-stage easy-to-deploy strategy for robust calibration of both intrinsic and extrinsic parameters of a projector. Two key components of the system are the automatic generation of projected light patterns and the incremental calibration process. Based on the incremental strategy, the calibration process first establishes a set of initial parameters, and then it upgrades these parameters incrementally using the projection and captured images of dynamically-generated calibration patterns. The scene-driven light patterns allow the system to adapt itself to the pose of the calibration target, such that the difficulty in feature detection is greatly lowered. The strategy forms a closed-loop system that performs self-correction as more and more observations become available. Compared to the conventional method, which requires a time-consuming process for the acquisition of dense pixel correspondences, the proposed method deploys a homography-based coordinate computation, allowing the calibration time to be dramatically reduced. The experimental results indicate that an improvement of 70% in reprojection errors is achievable and 95% of the calibration time can be saved.

Recently LiDAR-camera systems have rapidly emerged in many applications. The integration of laser range-finding technologies into existing vision systems enables a more comprehensive understanding of 3D structure of the environment. The advantage, however, relies on a good geometrical calibration between the LiDAR and the image sensors. In this paper we consider visual odometry, a discipline in computer vision and robotics, in the context of recently emerging online sensory calibration studies. By embedding the online calibration problem into a LiDAR-monocular visual odometry technique, the temporal change of extrinsic parameters can be tracked and compensated effectively.

This paper highlights the role of ground manifold modeling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single-disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different ground-manifold models (e.g., plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. This paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results, compared with the binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. The experimental results also include quantitative evaluations of the tradeoff between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions.