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Table 3 Summary table of some VO works in literature

From: Review of visual odometry: types, approaches, challenges, and applications

Reference

Camera type

Approach

VO estimation accuracy

Limitations

Gonzalez et al. (2013)

Two monocular cameras: downward-facing camera for displacement and front-facing camera for orientation estimation

Appearance-based approach (NCC template matching)

Error <3% of the total travelling distance and <8° average orientation error

False matches due to shadows and blur at velocity >1.5 m/s

Can’t deal with scale variance on non-smooth surfaces

Van Hamme et al. (2015)

Monocular camera

Feature-based approach (inverse perspective projection and Kalman filter for Tracking of features in the ground plane)

>8.5% translation error (for 800 m)

Significant rotational bias on some estimated trajectory segments due to non-planarity of the road environment in those segments

Scaramuzza and Siegwart (2008a)

Omnidirectional camera

Hybrid approach (tracking SIFT feature points from ground plane to estimate translation. Image appearance similarity measure (NCC, Manhattan and Euclidean distance) was used to estimate the rotation of the car)

Error is <2% of the distance traversed

5° average orientation error

Unavoidable visual odometry drift and deviation due to road humps that violate the planar motion assumption

Nistér et al. (2004)

Stereo camera

Feature-based approach (Detection of features independently in all frames and only allowed matches between salient features)

1.63% error over 380 m of the distance traversed

No mention

Howard (2008)

Stereo camera

Feature-based approach (Feature matching and employing stereo range data for inlier detection)

0.25% error over 400 m of the distance traversed

Self-Shadow leads to false-matches

It does not work effectively on vegetated environment

Nourani-Vatani and Borges (2011)

Monocular camera

Appearance-based approach (NCC multi-template matching which selects best template based on entropy)

Error <5% of total travelling distance

5° average heading error

Deficiency in dealing with scale variance at uneven surfaces

System can’t deal with sunny/shadow regions

Yu et al. (2011)

Monocular camera

Appearance-based approach (NCC rotated template matching)

1.38% distance error and 2.8° heading error

Cannot deal with image scale variance, shadows and blur

Nagatani et al. (2010)

Telecentric camera (which maintains the same field of ground area view, regardless of variation in camera height from ground

Appearance-based approach (cross correlation template matching)

<3% error indoor experiment

1.5% (for 100 m trajectory) at 0.4 m/s speed

Cannot estimate the camera height from ground variations

Zhang et al. (2014)

Monocular camera

Feature-based approach [tracking of features using Lucas Kanade Tomasi (LKT)]

Error is <1% of the distance traversed

Image scale uncertainty at complicated ground conditions for example loose soil floors