This paper proposes a new approach to scale-aware monocular visual odometry (VO) and extrinsic calibration using constraints on camera motion by vehicle kinematics. Main idea is to utilize the Ackermann steering model to observe absolute metric scale in turning motion. To describe motion of the camera attached to the vehicle, we first estimate unknown camera-vehicle relative pose by the proposed extrinsic calibration method. To stably observe scale, we detect turn regions and design an observer to estimate the absolute scale as a function of the camera rotation and direction of translational motion during turning. Using the observed scale, we propose an absolute scale recovery to estimate the unknown scale between turns. Because the proposed scale observer becomes singular near zero rotation, we conduct sensitivity analysis on the scale observer, and investigate appropriate conditions for stable scale estimation. For quantitative evaluation of the extrinsic calibration and the absolute scale recovery, we randomly generate synthetic driving datasets with various noise conditions, and evaluate the performance of each module statistically by Monte-Carlo simulations. To evaluate the overall performance, we implement our method and state-of-the-art monocular and stereo VO methods in the public outdoor driving KITTI dataset, and our method shows competitive scale recovery performance with no external sensor and no assumption on surroundings such as planar ground landmarks. To show promising applicability, we collect real-world driving datasets in two multi-floor underground parking lots, and demonstrate the accurate absolute scale recovery performance of our method in indoor driving situations.

This paper proposes an automatic and accuracy- enhanced extrinsic calibration method for 3D LiDARs with a range offset correction, which needs only an arbitrarily-shaped single planar board. One of the most exhaustive parts of existing LiDAR calibration procedures is to manually find target objects from massive point clouds. To obviate user interventions, we propose an automated planar board detection from LiDAR range images. To extract a target completely, we suppress outliers and restore rejected inliers of the target board by introducing a target completion method. We empirically find that range measurements of various LiDARs are mainly skewed by constant offset values. To compensate for this, we suggest a range offset model for each laser channel in calibration procedures. The relative pose between LiDARs and range offsets are jointly estimated by minimizing bi-directional point- to-board distances within the iterative re-weighted least squares (IRLS) framework. To verify the suggested range offset model, we obtain and analyze extensive real-world measurements. By conducting experiments using the various sensor configurations and shapes of boards, we quantitatively and qualitatively confirm accuracy and versatility of the proposed method by comparing with the state-of-the-art LiDAR calibration methods. All the source code and data used in the paper are available at : https://github.com/JunhaAgu/AutoL2LCalib.

This paper presents a new efficient algorithm which guarantees a solution for a class of multi-agent trajectory planning problems in obstacle-dense environments. Our algorithm combines the advantages of both grid-based and optimization-based approaches, and generates safe, dynamically feasible trajectories without suffering from an erroneous optimization setup such as imposing infeasible collision constraints. We adopt a sequential optimization method with dummy agents to improve the scalability of the algorithm, and utilize the convex hull property of Bernstein and relative Bernstein polynomial to replace non-convex collision avoidance constraints to convex ones. The proposed method can compute the trajectory for 64 agents on average 6.36 seconds with Intel Core i7-7700 @ 3.60GHz CPU and 16G RAM, and it reduces more than 50% of the objective cost compared to our previous work. We validate the proposed algorithm through simulation and flight tests.

We propose an efficient edge-based stereo visual odometry (VO) using multiple quadtrees created according to image gradient orientations. To characterize edges, we classify them into eight orientation groups according to their image gradient directions. Using the edge groups, we construct eight quadtrees and set overlapping areas belonging to adjacent quadtrees for robust and efficient matching. For further acceleration, previously visited tree nodes are stored and reused at the next iteration to warm-start. We propose an edge culling method to extract prominent edgelets and prune redundant edges. The camera motion is estimated by minimizing point-to-edge distances within a re-weighted iterative closest points (ICP) framework, and simultaneously, 3-D structures are recovered by static and temporal stereo settings. To analyze the effects of the proposed methods, we conduct extensive simulations with various settings. Quantitative results on public datasets confirm that our approach has competitive performance with state-of-the-art stereo methods. In addition, we demonstrate the practical values of our system in author-collected modern building scenes with curved edges only.