Mapping | Andreas Ziegler

Exploration Without Global Consistency Using Local Volume Consolidation

Tue, 03 Sep 2019 00:00:00 +0000

A Representation for Exploration that is Robust to State Estimate Drift

Mon, 30 Apr 2018 00:00:00 +0000

Motivation

Exploration is a fundamental task in the field of robotics. The goal is to build a map of a previously unknown environment. Two tasks have to be performed repeatedly for exploration: mapping the space the robot so far perceived; and planning where to go next. In this thesis we focus on the first task and the goal is to find a map representation that can deal with noisy state estimates. All so far presented map representations either assume perfect state estimates or need to rebuild the map after optimization.

Approach

To achieve this goal we build our representation with polygons. Our polygons represent the boundary between free known space and occupied space or unknown space and the inside of polygons is implicitly free space. We develop two approaches: The first approach builds a global map with polygons by continuously building the union of the polygon from the current field of view and the polygons of the so far explored space. The second approach works with polygons of the local field of view only. For every local polygon the frontiers, the obstacles and the free space is determined and the robot explores as long frontiers are present in the map.

Result

In this thesis we propose a novel representation that can deal with noisy state estimates and does not need to rebuild the map or parts of it, e.g. after a loop closure. By experiments we show that we achieve full coverage of the area to explore with frontier-based exploration, using our proposed representation.

Map Fusion for Collaborative UAV SLAM

Thu, 30 Mar 2017 00:00:00 +0000

Motivation

A correct and accurate common map is crucial for multiple robots to collaboratively performing tasks. In this semester project, an existing multi agent Simultaneous Localisation and Mapping (SLAM) system should be extended to fuse maps of single robots in a way that no false map alignment is guaranteed and that an optimal alignment is achieved by using multiple place matches. Also redundant information, a consequence of the fusion of two maps, should be removed in order to get a good performance of the optimization routines e.g. Bundle Adjustment (BA).

Approach

To achieve the first goal, a new approach is proposed which uses multiple KeyFrame Matchs (KFMs) to fuse two maps. This proposed approach also use a novel idea to spread the KFMs over a bigger area by skipping KeyFrames (KFs) between the detection of KFMs. To remove redundant information, KF culling is performed after all KFMs were detected and before the main map fusion. This way information which were present in both maps appear only once in the fused map and the Pose Graph Optimization (PGO) and the BA does not have unnecessary data to process. To reduce the runtime of the optimization part (PGO and BA), the usage of Powell’s dog leg (DL) non-linear least squares technique instead of the Levenberg-Marquardt (LM) optimization was evaluated and the system was adapted in a way, that the performance is increased.

Result

The proposed map fusion approach reduces drift and achieves better accuracy compared to the previous approach. With the implemented KF culling, the number of KFs which the PGO and the BA have to process is decreased significantly and therefore better timing is achieved. The usage of DL non-linear least squares technique reduced the runtime of the optimization furthermore.