Benchmark Problem "Laser SLAM - Bicocca_2009-02-25b"
Creator: GF
Labels: first batch, artificial light, static, indoor, SLAM
Perform a map building activity with SLAM (online), using the sensor data files provided below. Such files come from the Bicocca_2009-02-25b data-collection session; they include output and calibration information associated to laser range scanners (2 Sick units, 2 Hokuyo units), IMU, odometry, and the ground truth for the specified session.
Please note that the Bicocca_2009-02-25b session also includes other data files, not used for this Benchmark Problem.
This BP belongs to the very first batch of problems published by Rawseeds; other BPs will follow.
Solutions
| Absolute Trajectory Error | Mapping Error | Relative Pose Error | Rough Estimate of Complexity | Self Localization Error | |
|---|---|---|---|---|---|
| GMapping |
mean: 2.04178 stddev: 1.86768 CI low: 0 CI high: 7.64483 |
RPE-T mean in [m]: 0.41481 RPE-T stddev in [m]: 1.62238 RPE-R mean in [rad]: 0.01952 RPE-R stddev in [rad]: 0.034522 |
: 0 |
REC: 0 |
: 0 |
| Vasco Scan-Matcher |
mean: 2.03363 stddev: 1.22913 CI low: 0 CI high: 5.72103 |
: 0 |
RPE-T mean in [m^2]: 0.29595 RPE-T stddev in [m^2]: 0.800236 RPE-R mean in [rad^2]: 0.016807 RPE-R stddev in [rad^2]: 0.028054 |
: 0 |
: 0 |
| GraphSLAM |
mean : 0.38581 stddev: 0.32012 CI low: 0 CI high: 1.3462 |
: 0 |
RPE-T mean in [m2]: 0.042024 RPE-T stddev in [m2]: 0.041291 RPE-R mean in [rad2]: 0.009779 RPE-R stddev in [rad2]: 0.011821 |
: 0 |
: 0 |
Attached Files
- Matlab scripts and data to automatically compute some of the evaluation metrics, given the trajectory data output by a SLAM algorithm. Includes an extended ground truth covering the whole path of the robot, obtained by manual scan matching performed on the data from onboard LRFs (indoor datasets only). - _Rawseeds_Metrics_Computation_Toolkit
- Set #04 of sensor positions on the robot - _SensorPositions_04.tar.torrent
- Set #01 of file formats - _FileFormats_01.tar.torrent
- CAD drawings of the Bicocca location - _Drawings_02.dxf.bz2.torrent
- Ground truth (trajectory data) - Bicocca_2009-02-25b-GROUNDTRUTH.csv.bz2.torrent
- Front Hokuyo LRF: data - Bicocca_2009-02-25b-HOKUYO_FRONT.csv.bz2.torrent
- Rear Hokuyo LRF: data - Bicocca_2009-02-25b-HOKUYO_REAR.csv.bz2.torrent
- IMU: data - Bicocca_2009-02-25b-IMU_STRETCHED.csv.bz2.torrent
- Odometry: data - Bicocca_2009-02-25b-ODOMETRY_XYT.csv.bz2.torrent
- Front Sick LRF: data - Bicocca_2009-02-25b-SICK_FRONT.csv.bz2.torrent
- Rear Sick LRF: data - Bicocca_2009-02-25b-SICK_REAR.csv.bz2.torrent
- List of corner positions for the Bicocca location (extracted from the executive drawings) - _Bicocca-Corners.tar.torrent
Evaluation Methodologies
The provided solutions will be evaluated and scored with respect to the following evaluation methodologies:
- ATE compares the trajectory of a robot, as reconstructed by an algorithm using real sensor data as its input, to the actual trajectory (ground truth). ATE is a mandatory performance measure. Please ... - Absolute Trajectory Error
- ME compares the map of an environment, as reconstructed by an algorithm using real sensor data as its input, to the actual map of the location (ground truth). ME is a recommended performance measure. - Mapping Error
- RPE measures the accuracy of a SLAM result, as reconstructed by an algorithm using real sensor data as its input, by comparing the reconstructed relative transformations between nearby poses to the ac... - Relative Pose Error
- REC provides a basic estimate of how the running time of an algorithm (which uses real sensor data as its input) scales as the quantity of data available to be processed increases. REC is a mandatory... - Rough Estimate of Complexity
- SLE aims to evaluate the overall quality of a SLAM algorithm by actually using its output in a realistic application. The SLAM algorithm, fed with real sensor data from a robot, is used to build a map... - Self Localization Error
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