Sebastian Scherer

Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collection, informative path planning must be computed online and be responsive to new observations. In this work, we present IA-TIGRIS (Incremental and Adaptive Tree-based Information Gathering Using Informed Sampling), which is an incremental and adaptive sampling-based informative path planner designed for real-time onboard execution. Our approach leverages past planning efforts through incremental refinement while continuously adapting to updated belief maps. We additionally present detailed implementation and optimization insights to facilitate real-world deployment, along with an array of reward functions tailored to specific missions and behaviors. Extensive simulation results demonstrate IA-TIGRIS generates higher-quality paths compared to baseline methods. We validate our planner on two distinct hardware platforms: a hexarotor unmanned aerial vehicle (UAV) and a fixed-wing UAV, each having different motion models and configuration spaces. Our results show up to a 38% improvement in information gain compared to baseline methods, highlighting the planner's potential for deployment in real-world applications. Project website: ia-tigris.github.io.

Resilient and robust odometry is crucial for autonomous systems operating in complex and dynamic environments. Existing odometry systems often struggle with severe sensory degradations and extreme conditions such as smoke, sandstorms, snow, or low-light conditions, threatening both the safety and functionality of robots. To address these challenges, we present Super Odometry, a sensor fusion framework that dynamically adapts to varying levels of environmental degradation. Super Odometry uses a hierarchical structure to integrate four core modules from lower-level to higher-level adaptability, including adaptive feature selection, adaptive state direction selection, adaptive engine selection, and a learning-based inertial odometry. The inertial odometry, trained on more than 100 hours of heterogeneous robotic platforms, captures comprehensive motion dynamics. Super Odometry elevates the inertial measurement unit to equal importance with camera and light detection and ranging (LiDAR) systems in the sensor fusion framework, providing a reliable fallback when exteroceptive sensors fail. Super Odometry has been validated across 200 kilometers and 800 operational hours on a fleet of aerial, wheeled, and legged robots and under diverse sensor configurations, environmental degradation, and aggressive motion profiles. It marks an important step toward safe and long-term robotic autonomy in all-degraded environments.

Achieving persistent tracking of multiple dynamic targets over a large area poses major challenges for a single robot with limited sensing. As the robot moves to track different targets, those outside its field of view accumulate uncertainty, making them harder to recover. An effective planner must manage uncertainty over long horizons and prevent permanent loss of unseen targets. However, most existing methods rely on short horizons and assume small, bounded environments, leading to poor tracking performance in large-scale settings. We present a hierarchical planner for tracking multiple moving targets with an aerial vehicle. To handle non-static targets, our method incorporates motion models and uncertainty propagation during path execution for informed decision-making. We decompose multi-target tracking into sub-tasks of single-target search and detection. Our pipeline includes a novel coverage planner for searching within evolving belief regions and an estimation module that assesses each sub-task’s success likelihood. This formulation enables conversion of the active tracking problem into a Markov decision process (MDP) solved via a tree-based algorithm to determine sub-task sequences. Simulation results show our planner outperforms existing approaches, reducing final uncertainty by 11–70% across environments. We further validate practical deployability in a real-robot experiment. The project website is at https://yuanjunbin.github.io/hptracking/.

Given a query image, Visual Place Recognition (VPR) is the task of retrieving an image of the same place from a reference database with robustness to viewpoint and appearance changes. Recent works show that some VPR benchmarks are solved by methods using Vision-Foundation-Model backbones and trained on large-scale and diverse VPR-specific datasets. Several benchmarks remain challenging, particularly when the test environments differ significantly from the usual VPR training datasets. We propose a complementary, unexplored source of information to bridge the train-test domain gap, which can further improve the performance of State-of-the-Art (SOTA) VPR methods on such challenging benchmarks. Concretely, we identify that the test-time reference set, the "map", contains images and poses of the target domain, and must be available before the test-time query is received in several VPR applications. Therefore, we propose to perform simple Reference-Set-Finetuning (RSF) of VPR models on the map, boosting the SOTA (~2.3% increase on average for Recall@1) on these challenging datasets. Finetuned models retain generalization, and RSF works across diverse test datasets.

Inertial odometry (IO) using only Inertial Measurement Units (IMUs) offers a lightweight and cost-effective solution for Uncrewed Aerial Vehicle (UAV) applications, yet existing learning-based IO models often fail to generalize to UAVs due to the highly dynamic and non-linear-flight patterns that differ from pedestrian motion. In this work, we identify that the conventional practice of transforming raw IMU data to global coordinates undermines the observability of critical information in UAVs. By preserving the body-frame representation, our method achieves substantial performance improvements, with a 66.7% average increase in accuracy across three datasets. Furthermore, explicitly encoding attitude information into the motion network results in an additional 23.8% improvement over prior results. Combined with a data-driven IMU correction model (AirIMU) and an uncertainty-aware Extended Kalman Filter (EKF), our approach ensures robust state estimation under aggressive UAV maneuvers without relying on external sensors or control inputs. Notably, our method also demonstrates strong generalizability to unseen data, underscoring its potential for real-world UAV applications.

Future ground logistics missions will require multiple robots to travel in a convoy between locations. As each location may require a different number of robots (e.g. resupply vehicles), these missions will require a mutable convoy formation structure that may be divided to meet operational needs at each location. We model this mission type by modifying the vehicle routing problem with multiple synchronizations (VRPMS) to enforce convoy constraints (VRPMS-CC). This centralized approach to organizing and routing convoys is represented as a graph-based routing problem and then solved as a mixed integer program. A solution of the VRPMS-CC forms convoys by ensuring that agents participating in the same convoy remain spatially and temporally coupled, traversing the same edge of the graph simultaneously. We demonstrate our approach through numerical studies, where we route up to six simulated agents through twenty convoying tasks, and on robotic hardware. These demonstrations motivate two further contributions to specialize our approach to robotic systems. We introduce: 1) a warm-starting heuristic that improves solver times by up to eighty-nine percent and 2) an online multi-depot variant of the VRPMS-CC that responds to a priori unknown impassable environmental obstacles.