This paper builds a 3D occupancy voxel map from onboard #LiDAR to spot & track flying objects real time, robust across changing environments & target appearances. Demonstrated in autonomous aerial interception of fast-maneuvering #UAV
ieeexplore.ieee.org/document/107582…
#DroneDetection
![Deployment of the proposed detection and tracking algorithms as part of a cooperative navigation multirobot system in various environments [52]. (a) Warehouse environment. (b) Forest environment.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ttw5tcvxt52gbygtxrgvschk/bafkreiayfxmn5lnt5m2atqx7hiv63xzbfs3sgwbnxdphq5ao5jkd46zneq)
![Illustration of the general framework, in which the dashed box highlights our contribution. Given the current partial object model and a set of candidate views, an NBV algorithm (RSV) [1] selects the next view to visit. Our method then uses this target NBV and the current partial model to select an informative focus point and employ a whole-body control strategy to reach the target while keeping the focus point within the camera FoV with a visibility constraint [11], avoiding obstacles with VFIs [12], and mitigating local minima at obstacle boundaries with a circulation constraint [13].](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ttw5tcvxt52gbygtxrgvschk/bafkreieramgx4vdzffmrac6vaitkafet2wzvgh5tm5bjdwtu5vvf25l6om)
![Representation of bilevel optimization procedure for the robotic prosthesis. Left panel: The robotic knee torque, τ , is generated from the impedance control parameter values, which are obtained through real-time tuning by the RL controller. Lower right panel: This panel illustrates features of knee and thigh kinematics throughout a single gait cycle. For knee kinematics, superscript numbers 1–4 denote the respective phases within the gait cycle (i.e., STF, STE, SWF, and SWE), each corresponding to a knee feature. For thigh kinematics, the minimum value of the thigh angle acts as the feature. Top right panel: At the end of each bilevel optimization iteration, the cost function derived from IRL, each in a quadratic form, is utilized in the design of the RL controller. The implementation of the two interleaving procedures involving the inverse RL and forward RL is summarized in Algorithm 1. The RL controller’s inputs include kinematic features from the corresponding phase [defined in (5) and (6)], and its outputs involve adjustments to the impedance settings [defined in (3)].](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ttw5tcvxt52gbygtxrgvschk/bafkreicouj2gktsqqrlrflv7z4kwavlcomzy2uso32cntiuhx6fj3s3hdu)
![3-D reconstruction from a run of OKVIS2-X on the Spagna sequence of the VBR dataset [1]. Reconstruction with a LiDAR sensor (top) or with a depth network (bottom) to showcase the versatility of the presented system to different sensor modalities. The estimated trajectory is visualized in black. Furthermore, different colors per submap are used.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ttw5tcvxt52gbygtxrgvschk/bafkreiaacr5gj2htxq6jdagdq57bv3jq4iqdswoi5xiwjlnpvll5yn2y2a)
