In autonomous driving, a prior LiDAR map(PLM) is used as a powerful tool for correcting SLAM drift, but finding robust and accurate correspondences between cross modal sensors is a challenging problem. To address this cross-modality issue, this paper proposes a real-time plane-based stereo localization system with a PLM. In the proposed system, drift in visual pose estimation is eliminated through plane-based joint optimization and the registration module. Two types of planes are employed in this system: surfel, ensuring accuracy in a narrow domain, and global plane, providing robustness in a wide area. For accurate and robust matching between the visual map point and PLM, surfels and global planes in PLM are utilized collaboratively based on point-to-PLM distance. To reduce the computational cost of the registration module, a plane-to-plane drift estimation module is proposed. The performance of the proposed system is extensively validated across synthetic simulation and real-world indoor and outdoor datasets. We validate the effectiveness of each module through ablation studies and also assess the robustness against error that may exist in PLM and initial pose. In most of the validation, the proposed system shows more accurate and robust performance compared to the state-of-the-art methods.

Existence of symmetric objects, whose observation at different viewpoints can be identical, can deteriorate the performance of simultaneous localization and mapping (SLAM). This work proposes a system for robustly optimizing the pose of cameras and objects even in the presence of symmetric objects. We classify objects into three categories depending on their symmetry characteristics, which is efficient and effective in that it allows to deal with general objects and the objects in the same category can be associated with the same type of ambiguity. Then we extract only the unambiguous parameters corresponding to each category and use them in data association and joint optimization of the camera and object pose. The proposed approach provides significant robustness to the SLAM performance by removing the ambiguous parameters and utilizing as much useful geometric information as possible. Comparison with baseline algorithms confirms the superior performance of the proposed system in terms of object tracking and pose estimation, even in challenging scenarios where the baseline fails.

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.

Visual navigation has been widely used for state estimation of micro aerial vehicles (MAVs). For stable visual navigation, MAVs should generate perception-aware paths which guarantee enough visible landmarks. Many previous works on perception-aware path planning focused on sampling-based planners. However, they may suffer from sample inefficiency, which leads to computational burden for finding a global optimal path. To address this issue, we suggest a perception-aware path planner which utilizes topological information of environments. Since the topological class of a path and visible landmarks during traveling the path are closely related, the proposed algorithm checks distinctive topological classes to choose the class with abundant visual information. Topological graph is extracted from the generalized Voronoi diagram of the environment and initial paths with different topological classes are found. To evaluate the perception quality of the classes, we divide the initial path into discrete segments where the points in each segment share similar visual information. The optimal class with high perception quality is selected, and a graph-based planner is utilized to generate path within the class. With simulations and real-world experiments, we confirmed that the proposed method could guarantee accurate visual navigation compared with the perception-agnostic method while showing improved computational efficiency than the sampling-based perception-aware planner.

Multirobot simultaneous localization and mapping (SLAM) requires technical ingredients such as systematic construction of multiple SLAM systems and collection of information from each robot. In particular, map fusion is an essential process of multirobot SLAM that combines multiple local maps estimated by team robots into a global map. Fusion of multiple local maps is usually based on interloop detection that recognizes the same scene visited by multiple robots, or robot rendezvous where a member(s) of a robot team is observed in another member's images. This article proposes a collaborative monocular SLAM including a map fusion algorithm that utilizes rendezvous, which can happen when multirobot team members operate in close proximity. Unlike existing rendezvous-based approaches that require additional sensors, the proposed system uses a monocular camera only. Our system can recognize robot rendezvous using nonstatic features (NSFs) without fiducial markers to identify team robots. NSFs, which are abandoned as outliers in typical SLAM systems for not supporting ego-motion, can include relative bearing measurements between robots in a rendezvous situation. The proposed pipeline consists of the following: first, a feature identification module that extracts the relative bearing measurements between robots from NSFs consisting of anonymous bearing vectors with false positives, and second, a map fusion module that integrates the map from the observer robot with the maps from the observed robots using identified relative measurements. The feature identification module can operate quickly using the proposed alternating minimization algorithm formulated by two subproblems with closed-form solutions. The experimental results confirm that our collaborative monocular SLAM system recognizes rendezvous rapidly and robustly, and fuses local maps of team robots into a global map accurately.

In micro aerial vehicle (MAV) operations, the success of a mission is highly dependent on navigation performance, which has raised recent interests on navigation-aware path planning. One of the challenges lies in that optimal motions for successful navigation and the designated mission are often different in unknown, unstructured environments, and only sub-optimality may be obtained in each aspect. We aim to organize a two-MAV team that can effectively execute the mission and simultaneously guarantee navigation quality, which consists of a main-agent responsible for mission and a sub-agent for navigation of the team. Especially, this paper focuses on path planning of the sub-agent to provide navigational assistance to the main-agent using a monocular camera. We adopt a graph-based receding horizon planner to find a dynamically feasible path in order for the sub-agent to help the main-agent's navigation. In this process, we present a metric for evaluating the localization performance utilizing the distribution of the features projected to the image plane. We also design a map management strategy and pose-estimation support mechanism in a monocular camera setup, and validate their effectiveness in two scenarios.